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A fundamental challenge in realizing body-worn sensors is in providing an effective and long-lasting power supply. Issues regarding batteries have prompted researchers to investigate powering devices by extracting energy from the inertial movement of the human body. While previous studies have investigated the effect of generator location and wearer activity on harvestable power, they have not considered the orientation of the generator; this is the focus of this paper. Acceleration data collected across a sample population (10 participants) during different activities (walking and running) and generator location (five locations on the body) are presented. These data are processed to analyze the effect of orientation, and we find that it can significantly reduce the harvestable power. Subsequently, we propose and analyze how two degree-of-freedom generators can improve tolerance to rotation; results indicate that it can be improved by one order of magnitude. [ABSTRACT FROM PUBLISHER]

Poor water quality is a global concern, with agricultural practices the major contributor to reduced water quality with emissions of nutrient fluxes in to water systems. Using a collaborative framework to support catchment-scale water quality monitoring, control and management (WQMCM), individual sub-networks can learn and predict the impact of catchment events on their locality[1], allowing dynamic decision making for local irrigation strategies. Since resource constraints on network nodes (e.g. battery life, computing power etc) require a simplified predictive model, low-dimensional model parameters are derived from the existing National Resource Conservation Method (NRCS). An M5 decision tree algorithm is then used to develop predictive models for total discharge volume ( Q ), response start and duration ( t1 & td ). Evaluation of these models demonstrates high accuracy (84-94%) even for a small training set of under 100 samples for Q and td . However, for t1 , 300 samples are required to give adequate performance. [ABSTRACT FROM AUTHOR]

Purpose: This article provides rehabilitation professionals and engineers with a theoretical and pragmatic rationale for the inclusion of haptic feedback in the rehabilitation of central nervous system disorders affecting the hand. Method: A narrative review of haptic devices used in sensorimotor hand rehabilitation was undertaken. Presented papers were selected to outline and clarify the underlying somatosensory mechanisms underpinning these technologies and provide exemplars of the evidence to date. Results: Haptic devices provide kinaesthetic and/or tactile stimulation. Kinaesthetic haptics are beginning to be incorporated in central nervous system rehabilitation; however, there has been limited development of tactile haptics. Clinical research in haptic rehabilitation of the hand is embryonic but initial findings indicate potential clinical benefit. Conclusions: Haptic rehabilitation offers the potential to advance sensorimotor hand rehabilitation but both scientific and pragmatic developments are needed to ensure that its potential is realized. [ABSTRACT FROM AUTHOR]

Abstract: Inertial energy harvesting is an emerging technology that can power electronic devices using energy scavenged from the motion of the human body. Owing to the relatively low frequencies associated with body motion (<3Hz), the generated electrical power is typically in the range of a few μW; hence transduction must be optimized. Previous studies have investigated the effect of activity and harvester location on the obtained power; this work evaluates how power is also affected by the harvester''s orientation. Ten participants performed walking and running exercises, while tri-axial acceleration data were sampled at five locations on the body. The results show consistency in the optimal orientation of the harvester between people, but this orientation is not aligned with the axes of the body and limbs. During walking, the power harvested from the upper and lower body differs by an order of magnitude; however, this difference is less significant when running. [Copyright &y& Elsevier]

Abstract: This paper discusses an instrumented forearm crutch that has been developed to monitor a patient’s weight bearing over the full period of their recovery, and that can potentially be used in a home environment. The crutch measures the applied weight, crutch tilt, and hand position on the grip. Data are transmitted wirelessly to a remote computer, where they are processed and visualized in LabVIEW. The results obtained from a successful pilot study highlight both the need for such an instrumented crutch and its ability to measure the weight being applied through a patient’s lower limb. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its individual network involvement based on its own energy resources and the information contained in each packet. The information content is ascertained through a system of rules describing prospective events in the sensed environment, and how important such events are. While the packets deemed most important are propagated by all sensor nodes, low importance packets are handled by only the nodes with high energy reserves. Results obtained from simulations depicting a wireless sensor network used to monitor pump temperature in an industrial environment have shown that a considerable increase in the network lifetime and network connectivity can be obtained. The results also show that when coupled with a form of energy harvesting, our technique can enable perpetual network operation. [Copyright &y& Elsevier]

Wireless sensor nodes are heavily resource-constrained due to their edge form factor, which has motivated increasing battery life through low-power techniques. This paper proposes a power management method that leads to less energy consumption in an idle state than conventional power management systems used in wireless sensor nodes. We analyze and benchmark the power consumption between Sleep, Idle, and Run modes. To reduce sensor node power consumption, we develop fine-grained power modes (FGPM) with five states which modulate energy consumption according to the sensor node's communication status. We evaluate the proposed method on a test bench Mica2. As a result, the power consumed is 74.2% lower than that of conventional approaches. The proposed method targets the reduction of power consumption in IoT sensor modules with long sleep mode or short packet data in which most networks operate. [ABSTRACT FROM AUTHOR]

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%. [ABSTRACT FROM AUTHOR]

Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand. [ABSTRACT FROM AUTHOR]

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation. [ABSTRACT FROM AUTHOR]

The proliferation of "Things" over a network creates the Internet of Things (IoT), where sensors integrate to collect data from the environment over long periods of time. The growth of IoT applications will inevitably involve co-locating multiple wireless sensor networks, each serving different applications with, possibly, different needs and constraints. Since energy is scarce in sensor nodes equipped with non-rechargeable batteries, energy harvesting technologies have been the focus of research in recent years. However, new problems arise as a result of their wide spatio-temporal variation. Such a shortcoming can be avoided if co-located networks cooperate with each other and share their available energy. Due to their unique characteristics and different owners, recently, we proposed a negotiation approach to deal with conflict of preferences. Unfortunately, negotiation can be impractical with a large number of participants, especially in an open environment. Given this, we introduce a new partner selection technique based on multi-armed bandits (MAB), that enables each node to learn the strategy that optimises its energy resources in the long term. Our results show that the proposed solution allows networks to repeatedly learn the current best energy partner in a dynamic environment. The performance of such a technique is evaluated through simulation and shows that a network can achieve an efficiency of 72% against the optimal strategy in the most challenging scenario studied in this work. [ABSTRACT FROM AUTHOR]