Mobile services such as Augmented Reality (AR) and online gaming require high computing and storage resources with real-time data exchange with servers. However, mobile devices are resource-constrained and cloud computing incurs additional network latency which makes them dissatisfactory to use such mobile services. To tackle this challenge, service caching has been emerging as a critical technology that provides services by leasing resources from mobile edge computing (MEC) servers located close to the users. Unlike content caching, which stores data only for delivery, service caching utilizes both computing and storage resources, and thereby should be jointly considered with code offloading which offloads the processing workloads of mobile devices to servers. Therefore, the conventional caching strategy that caches the most popular services may not be the best choice, and the intertwined nature of service caching and code offloading policies makes the system operation complex. In this paper, we formulate an average cost (energy consumption, offloading payment) minimization problem of mobile devices constrained by service queue stability in a three-layer (mobile/MEC/cloud) network architecture. We apply a statistic-based dynamic optimization framework to derive an online-fashioned interTWined Interplay between Storage and Transmission, namely TWIST algorithm that jointly makes decisions of service caching, CPU clock frequency of mobile devices, and code offloading. Next, via theoretical analysis, we show the trade-off relationships between the average cost of mobile devices and service delay. Finally, extensive simulations demonstrate that TWIST can save up to 51% of cost for the same delay of 2.41sec. compared to the existing algorithms.

In this paper, we address the challenge of optimizing service routes for autonomous vehicles operating within edge-assisted cellular infrastructures. To tackle the complexity of resource-constrained and dynamic environments, we introduce a three-stage service routing model that spans uplink base stations, edge servers, and downlink base stations. We first prune infeasible links based on real-time resource availability and then logically expand the network into multi-layered structures to streamline route optimization. Building on this, we propose the SErvice Route Optimization (SERO) algorithm, which leverages Lagrangian dual decomposition to allocate resource-efficient service routes while preventing computational and network bottlenecks. Extensive simulations based on real-world city maps and vehicular traces from 2,502 vehicles demonstrate that SERO significantly reduces service failure rates—by up to 68.27% compared to existing approaches. Our findings underscore the importance of jointly optimizing communication and computation resources for scalable and reliable autonomous vehicle services in future B5G/6G networks.

Vision applications using deep neural network (DNN) models are increasingly prevalent in mobile devices like autonomous vehicles, drones, and smartphones. The quality of experience (QoE) for these applications is affected by the hardware performance of the mobile devices, fluctuating network conditions, and the characteristics of the DNN model. In this paper, we introduce the Parecon algorithm, which optimizes DNN model partitioning and frame resolution based on stochastic optimization adapting to both internal and external system dynamics. Parecon dynamically determines (i) DNN model partition point between the mobile device and the MEC server, (ii) resolution of the input frame, and (iii) number of processed frames per second, which have not been jointly addressed in previous studies. We theoretically demonstrate that Parecon optimizes three key QoE metrics, i.e., end-to-end (E2E) latency, accuracy, and throughput. Furthermore, we validate the effectiveness and superiority of Parecon over existing algorithms through trace-driven simulations and testbed implementation based on an embedded device and a high-end GPU server.

As deep learning technology becomes advanced, mobile vision applications, such as augmented reality (AR) or autonomous vehicles, are prevalent. The performance of such services highly depends on computing capability of different mobile devices, dynamic service requests, stochastic mobile network environment, and learning models. Existing studies have independently optimized such mobile resource allocation and learning model design with given other side of parameters and computing/network resources. However, they cannot reflect realistic mobile environments since the time-varying wireless channel and service requests are assumed to follow specific distributions. Without these unrealistic assumptions, we propose an algorithm that jointly optimizes learning models and process/network resources adapting to system dynamics, namely, VisionScaling by leveraging the state-of-the-art online convex optimization (OCO) framework. This VisionScaling jointly makes decisions on 1) the learning model and the size of input layer at learning-side and 2) the GPU clock frequency, the transmission rate, and the computation offloading policy at resource-side every time slot. We theoretically show that VisionScaling asymptotically converges to an offline optimal performance with satisfying sublinearity. Moreover, we demonstrate that VisionScaling saves at least 24% of dynamic regret which captures energy consumption and processed frames per second (PFPS) under mean average precision (mAP) constraint via real trace-driven simulations. Finally, we show that VisionScaling attains 30.8% energy saving and improves 39.7% PFPS while satisfying the target mAP on the testbed with Nvidia Jetson TX2 and an edge server equipped with high-end GPU.

The goal of this study is to minimize the average delay under the average energy consumption constraint in a single-queue and single-server wireless communication system with block fading channels. To this end, we formulate this problem as an infinite-horizon-constrained Markov decision process (CMDP). In our CMDP, we jointly consider the queue length and channel condition as the state. We apply the Lagrange multiplier method to transform the constrained optimization problem into an unconstrained optimization problem. Then, we prove that an optimal scheduling strategy is nondecreasing with respect to queue length and channel state. To obtain an optimal scheduling policy, an efficient reinforcement learning algorithm, the Structural-Optimistic$Q$-learning algorithm (SOQ), is proposed, which exploits the nondecreasing property of optimal policies by using policy projection. Finally, we analyze how to control the average energy consumption to satisfy a given energy consumption constraint. The simulation results show that the performance of the SOQ surpasses that of the traditional$Q$-learning algorithm in terms of the average cost during the learning phase.

There is increasing competition among cloud object storage service (COSS) providers as the demand for COSSs grows. However, existing pricing models offered by commercial COSS providers fail to effectively adapt to changing client demand and resource supply. Consequently, many COSS providers are still grappling with operational challenges in maximizing their profits, such as pricing policy, load balancing, server scheduling, and energy management. In this paper, we propose a novel approach called time-dependent pricing and scheduling (TD-PnS), which is based on the Lyapunov-drift-minus-profit technique. To maximize the profits of COSS providers,TD-PnSenables joint and dynamic decision-making across several key factors that have been dealt with separately so far:(i)service pricing,(ii)CPU clock scaling and encoding scheduling,(iii)network scheduling, and(iv)energy storage management. We propose an enhanced version ofTD-PnS, calledTD-PnS-Adv, further to improve other aspects, such as system stabilization. Finally, through trace-driven simulations utilizing a real dataset, we demonstrate the superior performance of the proposed algorithms compared to existing algorithms and pricing models in terms of profit maximization.

Recently, applications using artificial intelligence (AI) technique in mobile devices such as augmented reality have been extensively pervasive. The hardware specifications of mobile devices, dynamic service demands, stochastic network states, and characteristics of DNN (Deep Neural Network) models affect the quality of experience (QoE) of such applications. In this paper, we proposeCutEdge, that leverages a virtual queue-based Lyapunov optimization framework to jointly optimize DNN model partitioning between a mobile device and a mobile edge computing (MEC) server and processing/networking resources in a mobile device with respect to internal/external system dynamics. Specifically,CutEdgemakes decisions of(i)the partition point of DNN model between the mobile device and MEC server,(ii)GPU clock frequency, and(iii)transmission rates in a mobile device, simultaneously. Then, we theoretically show the optimal trade-off curves among energy consumption, throughput, and end-to-end latency yielded byCutEdgewhere such QoE metrics have not been jointly addressed in the previous studies. Moreover, we show the impact of joint optimization of three control parameters on the performances via real trace-driven simulations. Finally, we show the superiority ofCutEdgeover the existing algorithms by experiment on top of implemented testbed using an embedded AI device and an MEC server.

There is increasing competition among cloud object storage service (COSS) providers as demand for COSSs grows. While various pricing models exist for commercial COSS providers, they do not effectively adapt to changing client demand and resource supply. As a result, many COSS providers are still facing fundamental problems in operational strategy to maximize their profit. In this paper, we propose TD-PnS based on the Lyapunov-drift-minus-profit technique which jointly and dynamically makes decisions on (i) service pricing, (ii) CPU clock scaling and encoding scheduling, (iii) network scheduling, and (iv) energy storage management to maximize COSS provider's profits. We also propose an additional version of TD-PnS, namely TD-PnS-Adv, that adds realistic aspects such as system stabilization. Finally, through trace-driven simulation using real dataset, we demonstrate that the proposed algorithms outperform existing algorithms and pricing models in terms of profit.

Service caching has been emerging as a key technology that can overcome the hardware limitations of mobile devices by leasing resources from mobile edge computing (MEC) servers. This technology is inherently different from content caching where caching performance can be maximized by caching the most popular contents since most of the services require workload processing from CPU/GPU in servers. Thereby caching the most popular services on the MEC server may not be the best choice. Besides, when the service caching system is designed together with code offloading policy, the problem becomes more difficult because two control parameters are tightly intertwined with each other. In this paper, we formulate an average cost minimization problem of mobile devices constrained by service queue stability in a three-layer (mobile/MEC/cloud) network architecture. We apply a statistic-based dynamic optimization framework to derive an online-fashioned interTWined Interplay between Storage and Transmission, namely TWIST algorithm that jointly makes decisions of service caching, CPU/GPU clock frequency of mobile devices, and code offloading. Next, via theoretical analysis, we show the trade-off relationships between average cost of mobile devices and service delay. Finally, extensive simulations demonstrate that TWIST can save up to 51% of cost for the same delay 2.41sec. compared to the existing algorithms.

Computational resources are increasingly virtualized to enable computational tasks to be offloaded to remote facilities along the route between the source and destination. The principle that underlies traditional routing, i.e., that only networking resources need to be considered, may no longer be true in a virtualized environment. In this paper, we propose a framework for the efficient utilization of multi-resource infrastructures in which computational resources can be used via the network. Such a framework intrinsically calls for the joint consideration of networking and computational resources. In particular, we focus on unifying the controls in dynamic service chaining and multiple resource management, which are the key technologies in an integrated SDN/NFV architecture. We formulate a multi-path problem for choosing the resources to use in different services. The problem can be viewed as variational inequality using the Lagrange duality and saddle point theory. Based on this, we develop an extragradient-based algorithm that controls and splits the sending rate of each service. We prove that the algorithm converges to the optimal, minimizing the system cost while maximizing service utility. Simulations for diverse scenarios demonstrate that our algorithm achieves high QoS while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

Demand for using cloud object storage has been increasing in order to efficiently manage a large number of binary large objects (BLOBs), including videos, photos and documents. Although many companies and institutions are currently trying to utilize public cloud object storage services such as AWS Simple Storage Service (S3), most of existing encoding systems for safe storage of data have not been optimized for current cloud object storage architecture. In this article, we propose a novel dynamic extreme erasure encoding algorithm, namelyDexEncodingaiming to maximize the utility of clients where the encoding locations in the cloud storage architecture are dynamically optimized between gateway and storage servers with respect to the time-varying cloud environment. Here, the utility captures the satisfaction of clients for the speed of data storage and fairness among clients.DexEncodingefficiently resolves resource bottlenecks by adapting to the dynamic network, processing and storage resource availability and storage request. Real measurement-driven simulations demonstrate that the proposedDexEncodingalgorithm drastically outperforms that applied in the state-of-the-art object storage systems in a perspective of clients’ satisfaction.

As deep learning technology advances, mobile vision applications such as augmented reality or autonomous vehicles are widespread. The quality of experience (QoE) of such applications highly depends on hardware specification of mobile device, dynamic service requests, stochastic network status and characteristics of DNN model. In this paper, we propose an algorithm called RT-DMP that jointly optimizes DNN model partitioning and process/network resources adapting to system dynamics by leveraging virtual queue-based Lyapunov optimization framework. The RT-DMP jointly makes decisions on (i) partition point between a mobile device and an MEC server, (ii) mobile GPU clock frequency, and (iii) transmission rate through the wireless network every time slot. We theoretically show that RT-DMP optimally strikes the balance among three QoE metrics that are energy consumption, throughput and end-to-end latency, which has not been addressed in existing studies. Finally, we demonstrate the performance and feasibility of RT-DMP via trace-driven simulations and real testbed based on Nvidia Jetson TX2 and a high-end MEC server.

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namelyBEANS. We prove that the proposedBEANSensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possiblynegative submodular objective functionandnon-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff ofBEANSwith optimal and existing algorithms, and show thatBEANSattains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

Mobile computation offloading is emerging as a promising technology to enhance the computation power of mobile devices by borrowing processing resources from the cloud. However, using cloud resource is a double-edged sword because of the potentially enormous network energy consumption of mobile devices. In this paper, we study the mobile device resource management problem for application throughput fairness and energy efficiency in computation offloading environment. Our problem seeks to optimize task arrival rates, scheduling for local processing and offloading, CPU clock speed, and network interface selection, so as to maximize the energy-utility efficiency defined as achievable utility per unit energy consumption. The efficiency metric has a fractional form that is hard to deal with in general. To address this difficulty, we modify a general Lyapunov optimization technique and derive a series of short-term problems that change over time with respect to an unknown objective parameter. Then, we derive an offloading algorithm and prove that the algorithm maximizes the long-term energy-utility efficiency. Trace-driven simulations demonstrate that our algorithm achieves high-energy efficiency while maintaining throughput fairness among applications running on a mobile device.

Network function virtualization (NFV) and Computation offloading (CO) are state-of-the-art technologies for flexible utilization of networking and processing resources. These two technologies are closely related in that they enable multiple physical entities to process a function provided in a service, and the service (or end host) chooses which resources to use. In this paper, we propose a generalized dual-resource system, which unifies NFV service and CO service frameworks, and formulate a multi-path problem for choosing resources to use in NFV and CO services. The problem is reformulated as a variational inequality by using Lagrange dual theory and saddle point theory. Based on this formulation, we propose an extragradient-based algorithm that controls and splits the sending rate of a service. We prove that the algorithm converges to an optimal point where system cost minus service utility is minimized. Simulations under diverse scenarios demonstrate that our algorithm achieves high quality of service while reducing the system cost by jointly considering dual-resource coupling and service characteristics.

Deep intracranial tumor removal can be achieved if the neurosurgical robot has sufficient flexibility and stability. Towards achieving this goal, we have developed a spring-based continuum robot, namely a Minimally Invasive Neurosurgical Intracranial Robot (MINIR-II) with novel tendon routing and tunable stiffness for use in a magnetic resonance imaging (MRI) environment. The robot consists of a pair of springs in parallel, i.e., an inner inter-connected spring that promotes flexibility with decoupled segment motion and an outer spring that maintains its smooth curved shape during its interaction with the tissue. We propose a shape memory alloy (SMA) spring backbone that provides local stiffness control and a tendon routing configuration that enables independent segment locking. In this work, we also present a detailed local stiffness analysis of the SMA backbone and model the relationship between the resistive force at the robot tip and the tension in the tendon. We also demonstrate through experiments, the validity of our local stiffness model of the SMA backbone and the correlation between the tendon tension and the resistive force. We also performed MRI compatibility studies of the 3-segment MINIR-II robot by attaching it to a robotic platform that consists of SMA spring actuators with integrated water cooling modules.

Most existing content caching designs require accurate estimation of content popularity, which can be challenging in the dynamic mobile network environment. Moreover, emerging hierarchical network architecture enables us to enhance the content caching performance by opportunistically exploiting both cloud-centric and edge-centric caching. In this paper, we propose a hybrid content caching design that does not require the knowledge of content popularity. Specifically, our design optimizes the content caching locations, which can be original content servers, central cloud units (CUs) and base stations (BSs) where the design objective is to support as high average requested content data rates as possible subject to the finite service latency. We fulfill this design by employing the Lyapunov optimization approach to tackle an NP-hard caching control problem with the tight coupling between CU caching and BS caching control decisions. Toward this end, we propose algorithms in three specific caching scenarios by exploiting the submodularity property of the sum-weight objective function and the hierarchical caching structure. Moreover, we prove the proposed algorithms can achieve finite content service delay for all arrival rates within the constant fraction of capacity region using Lyapunov optimization technique. Furthermore, we propose practical and heuristic CU/BS caching algorithms to address a general caching scenario by inheriting the design rationale of the aforementioned performance-guaranteed algorithms. Trace-driven simulation demonstrates that our proposed hybrid CU/BS caching algorithms outperform the general popularity based caching algorithm and the independent caching algorithm in terms of average end-to-end service latency and backhaul/fronthaul load reduction ratios.

In this paper, we consider the content caching design without requiring historical content access information or content popularity profiles in a hierarchical cellular network architecture. Our design aims to dynamically select caching locations for different contents where caching locations can be content servers, cloud units (CUs), and base stations (BSs). Our design objective is to support as high content request rates as possible while maintaining the finite service time. To tackle this design problem, we employ the Lyapunov optimization method where the caching algorithm is developed by minimizing the Lyapunov drift of a quadratic Lyapunov function of virtual queue backlogs. This solution approach requires to solve a max-weight problem, which is an NP-hard and difficult problem to solve due to the coupling between CU caching and BS caching decisions. By exploiting the submodularity of the objective function, we propose a hybrid caching algorithm which achieves the constant approximation ratio to the optimal performance. Trace-driven simulation results demonstrate that the proposed joint CU/BS caching algorithm achieves almost the same performance with the exhaustive search and outperforms the independent caching algorithm and heuristic joint caching algorithms in terms of average end-to-end latency and backhaul load reduction ratio.

Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communication, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework “CarrierMix” that aggregates smart devices across multiple heterogeneous cellular carriers. Compared to the standalone operation, even without a buffering delay, CarrierMix allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of CarrierMix and its approximated heuristic. Trace-driven simulations on our experimental HSPA/EVDO/LTE network traces show that CarrierMix of five devices achieves up to 42 percent of energy reduction.

Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays. In this paper, we present Cedos, a practical delay-tolerant mobile network access architecture in which one can easily build a mobile app. Cedos consists of three components. First, it provides a familiar socket API whose semantics conforms to TCP, while the underlying protocol, D2TP, transparently handles network disruptions and delays in mobility. Second, Cedos allows the developers to explicitly exploit delays in mobile apps. App developers can express maximum user-specified delays in content download or use the API for real-time buffer management at opportunistic Wi-Fi usage. Third, for backward compatibility to existing TCP-based servers, Cedos provides D2Prox, a protocol-translation Web proxy. D2Prox allows intermittent connections on the mobile device side, but correctly translates Web transactions with traditional TCP servers. We demonstrate the practicality of Cedos by porting mobile Firefox and VLC video streaming client to using the API. We also implement delay/disruption-tolerant podcast client and run a field study with 50 people for eight weeks. We find that up to 92.4% of the podcast traffic is offloaded to Wi-Fi, and one can watch a streaming video in a moving train while offloading 48% of the content to Wi-Fi without a single pause.

The paper presents the use of shape memory alloy (SMA) spring actuators with real-time cooling to control the motion of the MINIR-II robot. A new actuation mechanism involving the passage of water as the cooling medium and air as the medium to drive out the water has been developed to facilitate real-time control of the springs. Control parameters, such as current, water flow rates, SMA pre-displacement, and gauge pressure of the compressed air, are identified from the SMA thermal model and from the actuation mechanism. In depth modeling and characterization have been performed regarding these parameters to optimize the robot motion speed. Forced water cooling has also been compared with forced air cooling and proved to be the superior method to achieve higher robot speed. An improved robot design and an MRI-compatible experimental platform have been developed for the implementation of the actuation mechanism.

Brain tumor, be it primary or metastatic, is usually life threatening for a person of any age. Primary surgical resection which is one of the most effective ways of treating brain tumors can have tremendously increased success rate if the appropriate imaging modality is used for complete tumor resection. Magnetic resonance imaging (MRI) is the imaging modality of choice for brain tumor imaging because of its excellent soft-tissue contrast. MRI combined with continuum soft robotics has immense potential to be the next major technological breakthrough in the field of brain cancer diagnosis and therapy. In this work, we present the design, kinematic, and force analysis of a flexible spring-based minimally invasive neurosurgical intracranial robot (MINIR-II). It is comprised of an inter-connected inner spring and an outer spring and is connected to actively cooled shape memory alloy spring actuators via tendon driven mechanism. Our robot has three serially connected 2-DoF segments which can be independently controlled due to the central tendon routing configuration. The kinematic and force analysis of the robot and the independent segment control were verified by experiments. Robot motion under forced cooling of SMA springs was evaluated as well as the MRI compatibility of the robot and its motion capability in brainlike gelatin environment.

Brain tumor, be it primary or metastatic, is usually life threatening for a person at any age. The risks involved in carrying out surgery within a brain can cause severe anxiety in patients. However, primary surgical resection which is one of the most effective ways of treating brain tumors can have a tremendously increased success rate if the appropriate imaging modality is used for complete tumor resection. Magnetic resonance imaging (MRI) is the imaging modality of choice for brain tumor imaging because of its excellent soft-tissue contrast. MRI combined with continuum soft robotics has immense potential to be the next major technological breakthrough in the field of brain cancer diagnosis and therapy. In this work, we present the design and kinematic analysis of a flexible spring-based minimally invasive neurosurgical intracranial robot (MINIR-II). It is comprised of an inter-connected inner spring and an outer spring to enable motion in three dimensions (3D). Our design provides improved dexterity with higher degrees of freedom (DoFs) and independent joint control because of the centrally routed tendon configuration. Since the robot itself is made of plastic (except the electrocautery probes and SMA spring actuators), it is MRI-compatible and allows surgeons to track the real-time location of the robot in the brain and to reach the brain tumor target. The inter-connected spring and the outer spring are manufactured individually in a single piece using rapid prototyping technology at low cost to make it disposable after single use. Our three-segment robot has two DoFs at each segment with both joints controlled by two pairs of MRI-compatible SMA spring actuators. We also present a detailed kinematic analysis of the robot, simulation of the robot motion, and experimental evaluation of the robot motion using vision feedback.

Delay-tolerant Wi-Fi offloading is known to improve overall mobile network bandwidth at low delay and low cost. Yet, in reality, we rarely find mobile apps that fully support opportunistic Wi-Fi access. This is mainly because it is still challenging to develop delay-tolerant mobile apps due to the complexity of handling network disruptions and delays.

Mobile cloud computing (MCC) has been proposed to offload heavy computing jobs of mobile devices to cloud servers managed by cloud service provider (CSP), which enables the mobile devices to save energy and processing delay. Heretofore, cloud offloading policies in mobile devices and pricing/scheduling in CSP have been independently addressed. This paper is first to jointly account for both sides of mobile users and CSP in a unified mobile cloud computing framework. By invoking “Lyapunov drift-plus-penalty” technique, we propose dual-side control algorithms for the mobile users and CSP in two different scenarios: (i) In non-cooperation scenario, we propose a NC-UC algorithm for the mobile users and a NC-CC algorithm for the CSP to minimize each cost for given delay constraints. (ii) In cooperation scenario, we suggest a CP-JC algorithm for both cloud users and CSP to minimize the sum costs of them for given delay constraints. Trace-driven simulations demonstrate that NC-UC saves minimum 63% of cost by trading 8MB of average queue lengths when compared with the existing algorithms, and NC-CC achieves 71% of profit gain when compared with the same delay of existing scheme; moreover, the cooperation enables them to save additional costs and delays.

To cope with increasing energy consumption in mobile devices, the mobile cloud offloading has received considerable attention from its ability to offload processing tasks of mobile devices to cloud servers, and previous studies have focused on single type tasks in fixed network environments. However, real network environments are spatio-temporally varying, and typical mobile devices have not only various types of tasks, e.g., network traffic, cloud offloadable/nonoffloadable workloads but also capabilities of CPU frequency scaling and network interface selection between WiFi and cellular. In this paper, we first jointly consider the following three dynamic problems in real mobile environments: 1) cloud offloading policy, i.e., determining to use local CPU resources or cloud resources; 2) allocation of tasks to transmit through networks and to process in local CPU; and 3) CPU clock speed and network interface controls. We propose a DREAM algorithm by invoking the Lyapunov optimization and mathematically prove that it minimizes CPU and network energy for given delay constraints. Trace-driven simulation based on real measurements demonstrates that DREAM can save over 35% of total energy than existing algorithms with the same delay. We also design DREAM architecture and demonstrate the applicability of DREAM in practice.

Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communication, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework “PhonePool” that aggregates smart devices across multiple cellular providers. Compared to the standalone operation, even without a buffering delay, PhonePool allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of PhonePool and its approximated heuristic. Trace-driven simulations on our experimental HSPA/EVDO/LTE network traces show that PhonePool of 5 devices achieves up to 42% of energy reduction.

To obtain the quantitative mechanical information, robotic palpation systems have been studied. The aim of this study was to evaluate the reliability of the mechanical mapping using correlation with pathological cancer suspected maps. A total of 60 indentations were performed on 5 specimens taken during radical prostatectomy with a robotic palpation system. Suspected cancer lesions based on the mechanical properties were compared to those of pathological results. The concordance rate was 81.7 % (98/120). Sensitivity and specificity were 93.5 % (29/36) and 91.6 % (22/24). Positive predictive value (PPV) and negative predictive value (NPV) were 93.5 % (29/31) and 75.8 % (22/29). As a result, the mechanically suspected lesions are in close agreement with those of pathological results. This study may contribute on technological progress for overcoming limitations which include many complications due to non-targeted systemic biopsy and late detection of prostate cancer.

Microinjection and biopsy of oocytes and embryos in Assisted Reproductive Technology (ART) require highly delicate handling of cells. In particular, efficient control of cell orientation is necessary to maintain their integrity during tool penetration, which currently remains challenging to accomplish by the existing method of repeated aspiration/release via micropipette. We present a microfluidic platform to automate the process of cell orientation control and trapping by means of hydrodyanmic force and vision-based position control. The device is accessible by conventional micropipettes via a cavity, allowing immobilized cells to be operated on. An orientation control algorithm based on the movement of the embryo within the microchannel is proposed. Visual tracking of the polar body is used to provide the information of cell orientation. Experimental results with mouse embryos indicate that cell orientation can be systematically controlled autonomously without human intervention and therefore provides a framework for further development of robotics approach to precise manipulation of microparticles within microfluidic devices.

Robotic palpation is of major interest as a medical technique that could replace subjective palpation and tactile sensation by yielding precisely controlled palpation to tissues and quantitative tactile feedback acquisition. Palpation results and biomechanics based mechanical property characterization are possible solutions that could enable the acquisition of objective and quantitative information on abnormal tissue localization during diagnosis and surgery. This paper presents an integrated approach for robotic palpation and mechanical property characterization. To validate the proposed methods, robotic palpation experiments on silicone soft-tissue phantoms with embedded hard inclusions were performed using a robotic palpation system, and the force responses of the phantoms were measured. Furthermore, we carried out a numerical analysis simulating the experiments and estimating the objective and quantitative properties of the tissues.