Power Management for Kinetic Energy Harvesting IoT

Human kinetic energy is considered to be a promising green energy source to enable human-powered Internet of Things (IoT), as constrained lifetime has become a bottleneck problem for IoT devices. However, the scarce energy collected by human motion severely restricts the operation of human-powered IoT and stresses the need for an optimized inertial harvester to provide more energy from human daily activities. In this paper, we investigate the feasibility and efficiency of using a single frequency inertial energy harvester, which is optimized based on a typical one-day motion of a human subject, to harvest kinetic energy from multiple-day activities of the same human subject. To facilitate this investigation, we propose a novel optimization framework to maximize the harvested power from human daily motion using a single-frequency energy harvester. By analyzing the frequency characteristics of human daily motion and the inertial harvester model, the optimal inertial harvester parameters are determined to maximize power generation from a typical one-day motion, and are used to harvest power from the same human subject's motion of other days. The real world human motion dataset is used for evaluation. The results demonstrate that the propose method can maximize power generated from one-day motion. Furthermore, the optimal harvester parameters determined by one-day trace can also achieve near-optimal harvested power from other days.

Energy harvesting technology provides a promising solution to enable internet of battery-less things (IoBT), as the lifetime and size of batteries become major limiting factors in the design and effective operation of internet of things (IoT). However, with constrained energy buffer size, the variation of ambient energy availability and wireless communication cast adverse effect on the operation of IoBT. There is a pressing demand for developing IoBT-specialized power management. In this paper, we propose a novel predictive power management (PPM) framework combining optimal working point, deviation aware predictive energy allocation, and energy efficient transmission power control. The optimal working point guarantees minimum power loss of IoBT systems. By predictively budgeting the available energy and using the optimal working point as a set-point, PPM mitigates the prediction error so that both power failure time and system power loss is minimized. The transmission power control module of PPM improves energy efficiency by dynamically selecting optimal transmission power level with minimum energy consumption. Real-world harvesting profiles are tested to validate the effectiveness of PPM. The results indicate that compared with the previous predictive power managers, PPM incurs up to $17.49\%$ reduction in system power loss and $93.88\%$ less power failure time while maintaining a high energy utilization rate. PPM also achieves $9.4\%$ to $23.22\%$ of maximum improvement of transmission energy efficiency compared with the state-of-the-art transmission power control schemes.

Trust management in social Internet of Things across domains

As the complexity of Internet of Things (IoT) systems increases, a large variety of tools and technologies for IoT management are making their way into both research setups and the market. IoT management solutions must consider the resource restrictions of embedded devices, as well as their heterogeneity and network dynamics. With these in mind, the Internet Engineering Task Force developed several standards targeting the integration and interoperation of heterogeneous devices, such as the Representational State Transfer Configuration Protocol (RESTCONF) or the Constrained Application Protocol (CoAP) Management Interface. Concurrently, the Open Mobile Alliance developed the Lightweight Machine-to-Machine protocol, for IoT device management. This paper provides a comprehensive, up-to-date overview of IoT management technologies, frameworks and protocols. Also, it proposes a taxonomy for IoT devices management. In addition to presenting the various solutions, the paper provides comparative views, standardization timeline, and market analysis. The presented analysis ranges from traditional network management protocols, such as Simple Network Management Protocol, to the newest IoT management and configuration protocols, such as CoAP Management Interface and Lightweight Machine-to-Machine protocols. Moreover, this survey identifies remaining challenges and solutions offered by recent management protocols, not covered by previous surveys.

A systematic survey of Internet of Things frameworks for smart city applications

Internet of Things (IoT) is a cooperative of small sensors and actuators and any other small devices that provide control and monitor for many aspects in real life via internet. These devices provide big data that need a high capabilities system to serve such as cloud computing. However, cloud computing itself cannot satisfy all of IoT requirement such as low latency, mobility, etc. To solve this problem, researchers proposed to add new layer called fog computing layer that sit between IoT layer and cloud computing. Fog computing is a micro data center that support cloud computing to satisfy IoT requirement. Fog computing is still in the infancy level and has many challenges that need to be resolved. In this paper, a survey that focus on power management and security behavior in both Fog computing and IoT has been conducted. Although there are many proposed methods and techniques in these fields, the survey showed that both systems suffer from weaknesses in security and power management and need to be improved.

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.

Internet of Things (IoT) is going to be an integral part of connected living and industrialization. Already several billion devices are connected in the IoT framework and this number is growing very fast. Rigidity of current network infrastructure limits the enormous possibilities which IoT can deliver. Software defined networking (SDN) with its agility and elasticity appears promising networking technique for the realization of robust IoT. SDN is quite suitable for the control and management of IoT. However, there are several issues and challenges pertaining to the implementation of SDN based IoT ecosystem. In this article, we present the issues and challenges faced by the network designers while deploying SDN over the IoT.

The efficient and reliable human-centred design of products and processes is a major goal of the manufacturing industry. Thus, numerous aspects related to performance, safety and ergonomics, need to be verified using Simulation and Virtual Reality techniques, in the context of the product development procedure. The realistic and accurate representation of human motion in Virtual Environment is crucial for the reliability of the simulation results. In this context, this dissertation focuses on the design and development of a novel methodology for human motion modelling, based on the adaptation of a given motion of a digital human model to new anthropometrics and environment’s constraints (related to virtual prototype or workspace). The proposed approach aims at the generation of realistic and reliable digital human motions in order to drive computer manikins into a Virtual Environment, so as to obtain reliable evaluation results during ergonomic design of a product or a production line’s workspace. The introductory chapter presents both the importance and the limitations of the ergonomic design using computer manikins, which consisted the major motivation of this research work. State-of-the-art is presented next, concerning other approaches related to human motion modeling using computer manikins, as well as software tools for digital human modeling and ergonomic design. Next chapter presents an extensive analysis, which focuses on the better understanding of the human motion. This analysis is based on a Statistical Design Of Experiments (SDoE) and makes use of experimental motion captured data. Analysis of Variance (ANOVA) was performed for the determination of the impact factor of the anthropometric parameters influencing the human motion path. Semi-empirical additive models was developed next, based on the results of this analysis, which connects the effect of anthropometrics with the trajectories of the markers that are attached on the human body during the motion capture procedure. The composing of the proposed motion modelling methodology is following. Given that human motion is analysed by a set of sequential motion frames, the modelling methodology aims at the generation of digital human’s postures for each frame of the desirable motion scenario. Motion scenario is each possible combination of “task – computer manikin – environment”. For the creation of a new motion’s frame, the algorithm of the methodology generates alternative postures, ensuring the rejection of non-realistic and constraint-violating postures. The basic concept of the modelling methodology is based on the multi-criteria decision making, which is used for the alternatives’ evaluation and the selection of the best-ranked human postures that constitute the new human motion. The criteria concern both the extensionality of the new motion and the satisfaction of the new constraints, related to the geometric modifications of the working environment. The description of the primary and secondary components of the implemented system, as well as their detailed design are presented next. The developed system consists of the following primary components: i)the data base, which includes reference motions, computer manikins, virtual environments and tasks , ii)the alternative generation mechanism, which takes into account the new constraints, iii)the evaluation criteria of alternatives, which are related to joint angles’ and end-effector’s similarity, iv)the decision matrix, which calculates the evaluation score of each alternative posture, based on the criteria, v)the aggregation mechanism, which calculates the utility score of each alternative, based on the evaluation scores and the weights of the criteria, vi)the ranking mechanism, which sorts the alternatives based on the utility score and selects the best-ranked alternative for each motion frame. The developed system enables the creation of adapted motions for digital humans that satisfies the new conditions and constraints. The new conditions and constraints come from the modification of the anthropometrics of the digital human model that realize the motion and/or the modification of the shape/geometry of the working environment. The evaluation of the proposed methodology’s efficiency is illustrated through a set of experiments through the pilot application coming from the automotive industry. The pilot application aims at the ergonomic evaluation of the interior design of a passenger car, focusing mainly on the position optimization for the driver’s seat and the door’s handle. The evaluation demonstrates the prediction capabilities of the algorithm, when both anthropometrics and environment parameters are modified. The algorithm generates accurate and realistic human motions that can be efficiently used in order to improve the computer-aided ergonomic design of manufacturing products and processes.

This paper proposes the contactless measurement scheme for human body motion interface including twisting motion for a personal motility. We have already proposed the saddle type human body motion interface. This interface uses not only conventional translational human motions but also twisting motion, namely it makes full use of the human motion characteristics. The mechanism of the interface consist of the saddle and universal joint connecting the saddle and personal mobility, and tracing hip motion. Due to these features, the proposed interface shows a potential to realize intuitive operation in the basic experiment. However, the problem has also remained: there are play between the saddle and buttocks of the user, and backlash of the saddle mechanism. This problem prevents a small human motion from measurment. This paper, therefore, propose the contactless measurement of human body motion including twisting motion. The proposed measurement scheme adopts the Laser scanner for detecting hip motion. The measured cloud points are fitted to the line by least square method. Then we can measure the human motion by using obtained inclination and the center of gravity.

With the recent development in cloud computing and adoption of the Internet of Things (IoT), the number of encoding and monitoring devices on IoT are expected to remain on the rise. This leads to unimaginable volume of data on IoT, thus affecting the performance of IoT networks. To mitigate this, several network arrangements and privacy-preserving security schemes that engage data aggregation to secure and reduce traffics in IoT or other forms of network had been proposed. However, most of these network arrangements not only incur high data traffic but also the employed data aggregation schemes are incapable of aggregating heterogenous IoT devices data. To address this challenge, we propose an efficient approach and framework for a secure IoT’s data management. The framework consists of a cascaded look ahead fog devices, which effectively solve problems associated with data locality and information management in IoT. It frequently keeps used data in closer and computationally cheaper fog levels while rarely used data are pushed to cloud data center. An effective aggregation and de-aggregation technique which allows aggregation of heterogenous devices’ data and de-aggregation of individual device data was proposed. Extensive performance analysis and evaluations are conducted, and the results indicated that the approach not only secure IoT network but with relatively low latency.

Aggregated distributed energy resources are the subject of much interest in the energy industry and are expected to play an important role in meeting our future energy needs by changing how we use, distribute and generate electricity. This energy future includes an increased amount of energy from renewable resources, load management techniques to improve resiliency and reliability, and distributed energy storage and generation capabilities that can be managed to meet the needs of the grid as well as individual customers. These energy assets are commonly referred to as Distributed Energy Resources (DER). DERs rely on a means to communicate information between an energy provider and multitudes of devices. Today DER control systems are typically vendor-specific, using custom hardware and software solutions. As a result, customers are locked into communication transport protocols, applications, tools, and data formats. Today’s systems are often difficult to extend to meet new application requirements, resulting in stranded assets when business requirements or energy management models evolve. By partnering with industry advisors and researchers, an implementation DER research platform was developed called the Smart Energy Framework (SEF). The hypothesis of this research was that an open source Internet of Things (IoT) framework could play a role in creating a commodity-based eco-system for DER assets that would reduce costs and provide interoperable products. SEF is based on the AllJoynTM IoT open source framework. The demonstration system incorporated DER assets, specifically batteries and smart water heaters. To verify the behavior of the distributed system, models of water heaters and batteries were also developed. An IoT interface for communicating between the assets and a control server was defined. This interface supports a series of “events” and telemetry reporting, similar to those defined by current smart grid communication standards. The results of this effort demonstrated the feasibility and application potential of using IoT frameworks for the creation of commodity-based DER systems. All of the identified commodity-based system requirements were met by the AllJoyn framework. By having commodity solutions, small vendors can enter the market and the cost of implementation for all parties is reduced. Utilities and aggregators can choose from multiple interoperable products reducing the risk of stranded assets. Based on this research it is recommended that interfaces based on existing smart grid communication protocol standards be created for these emerging IoT frameworks. These interfaces should be standardized as part of the IoT framework allowing for interoperability testing and certification. Similarly, IoT frameworks are introducing application level security. This type of security is needed for protecting application and platforms and will be important moving forward. Recommendations are that along with DER-based data model interfaces, platform and application security requirements also be prescribed when IoT devices support DER applications.

This paper surveys the current state of data management in the Internet of Things (IoT). It begins by outlining the challenges and opportunities that data management in IoT presents. Firstly, data management deals with the technical challenges and solutions related to data management in IoT, including data acquisition, storage, and integration. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Secondly, the requirement of IoT for data management extends offline storage, query processing, and transaction management activities into online-offline communication and storage dual operations, and the idea of data management is broadened. This is accomplished by IPv6, as well as IoT-specific capabilities and protocols including CoAP, HTTP, and WebSocket. Users may track, monitor, and manage devices with Internet of Things (IoT) device management, ensuring that they operate effectively and securely after deployment. Finally, the paper discusses the various applications of IoT based on the concept of data management in IoT. Numerous more objects, including wearables, medical equipment, houses, cities, farms, industries, and workplaces, are being interacted with by billions of sensors. The IoT platforms assist in establishing and maintaining criteria to enhance and preserve data appropriately. The paper concludes with a set of recommendations for the development of effective data management strategies in the context of IoT. Smart gadgets automate processes so we may save time by controlling the environment. The most valuable data is protected by edge devices for data management, which also lowers bandwidth costs. These also offer excellent performance, data ownership, and cheap maintenance costs.

A standing-up assistance chair without any electric actuator for the weak-muscle people, such as elderly people and patients, was developed in this research. The mechanism was designed based on the real human standing-up motion. To satisfy the motion, the design principles were considered, 1) the timing of opportunity of leaning forward motion (forward movement of shoulder) before lifting the hip joint is 40% of standing-up phase; 2) The trajectory of user’s hip joint is a straight line with angle of 45 [deg]; 3) the device supports the user until his/her knee joint angle achieve around 60 [deg]. The mechanism with eight linkages based on Hart’s exact straight-line mechanism was considered, which can realize the motion of the approximate straight line with an angle of 45 [deg]. Based on the real human motion and design principles, the proper link lengths of the device were simulated to observe the effect of hip joint angle and the trajectory of COG. We confirmed that during the phase of standing leaning forward and recovered motion can be assisted with an adequate method. By using our assistance chair, the torque on each joint can be decreased. For different sizes of users, we intend to build S, M, L types of the device. Based on the average human body data [3].

The purpose of this paper is to describe human motions and emotions that appear on real video images with compact and informative representations. We aimed to recognize expressive motions and analyze the relationship between human body features and emotions. We propose a new descriptor vector for expressive human motions inspired from the Laban movement analysis method (LMA), a descriptive language with an underlying semantics that allows to qualify human motion in its different aspects. The proposed descriptor is fed into a machine learning framework including, random decision forest, multi-layer perceptron and two multiclass support vector machines methods. We evaluated our descriptor first for motion recognition and second for emotion recognition from the analysis of expressive body movements. Preliminary experiments with three public datasets, MSRC-12, MSR Action 3D and UTkinect, showed that our model performs better than many existing motion recognition methods. We also built a dataset composed of 10 control motions (move, turn left, turn right, stop, sit down, wave, dance, introduce yourself, increase velocity, decrease velocity). We tested our descriptor vector and achieved high recognition performance. In the second experimental part, we evaluated our descriptor with a dataset composed of expressive gestures performed with four basic emotions selected from Russell’s Circumplex model of affect (happy, angry, sad and calm). The same machine learning methods were used for human emotions recognition based on expressive motions. A 3D virtual avatar was introduced to reproduce human body motions, and three aspects were analyzed (1) how expressed emotions are classified by humans, (2) how motion descriptor is evaluated by humans, (3) what is the relationship between human emotions and motion features.