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The CDS architecture implemented in the Android software stack. The different employed Android API components are labeled.
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![Figure 4. The functional blocks of the proposed CC, adapted from [5]. A...](publication/331200490/figure/fig2/AS:728014367240194@1550583559937/The-functional-blocks-of-the-proposed-CC-adapted-from-5-A-pool-of-explore-exploit_Q320.jpg)
Mobile Edge Computing (MEC) relates to the deployment of decision-making processes at the network edge or mobile devices rather than in a centralized network entity like the cloud. This paradigm shift is acknowledged as one key pillar to enable autonomous operation and self-awareness in mobile devices in IoT. Under this paradigm, we focus on mobili...
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... that Android lacks ways to cancel location requests that are not likely to be obtained, leading to unnecessary energy overhead. Figure 9 shows the CDS blocks implemented as a middleware using different Android API components across multiple OS layers for isolating the complexity of sensors access and power management. Here, the SA eased the implementation of the data fusion scheme and the nested FSMs shown in Figure 8. ...
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... Therefore, ML and other data preprocessing needs are often offloaded to cloud-based solutions, often associated with their own challenges [151]. Furthermore, with the advancements in research areas, such as smart cities and transportation, specific sensors in IoT devices, especially location sensors, can play a critical role in tackling scalability issues but often consume significant amounts of power from smart devices [152]. The proposed on-device CIoT framework aims to amortize the energy requirements of mobile systems, such as smartphones, by learning an expanded spatiotemporal model of user mobility from detected stay points and frequently visited areas. ...
... Finally, the executive is explained as the mechanism responsible for dynamically adapting the system's behavior toward the defined goal by selecting the most appropriate policy for the cognitive actions to actuate in the environment. Further details and specifics about the practical implementation of the framework, specifically the cognitive controller, can be found in another study published by Pérez-Torres et al. [152]. ...
The field of cognitive dynamic systems (CDSs) is an emerging area of research, whereby engineering learns from neuroscience. Under this framework, engineering systems are configured in a manner that mimics the human brain and improves the utility and performance of traditional systems. In essence, a CDS builds on Fuster’s paradigm of cognition and is fulfilled with the presence of five cognitive processes: the perception-action cycle, memory, attention, intelligence, and language. When augmented with these processes, a system can be classified as a CDS and is afforded the capabilities of processing information and learning from experience through continued interactions with the environment. Tremendous benefit from adopting the CDS framework has been observed in the literature, especially in the fields of cognitive radio and cognitive radar. More recently, the framework has been extended to other areas, such as control theory, risk control, and the Internet of Things; where the potential for drastic performance improvements has been evident in the literature. This comprehensive article presents a thorough background and exposition of the CDS framework and each field where it has been applied. In addition, we provide a comprehensive review of the recent advancements and related works in each domain by summarizing the key facts relating to the methodologies, findings, and limitations of the surveyed papers. Our novel contributions involve being the first source of centralized information on this topic and forming the foundation for future research efforts by presenting suggestions regarding worthwhile avenues for further investigation.
... In [36] a method was proposed for IoT devices to enable energy-aware sampling of GPS signal and processing of location data. That method utilizes cognitive control and event recognition techniques. ...
The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction.
... Recently, event-and self-triggered sampling [21][22][23][24][25][26][27][28][29][30][31][32][33] have been introduced into multi-agent coordinated control [32,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], and its sampling times are determined by event-triggered conditions rather than by synchronous or asynchronous digital clock signals. In comparison with traditional periodic sampled-data control, distributed event-triggered control performs data sampling according to the needs of agents, avoiding redundant sampling at unnecessary times, thereby saving system resources. ...
This paper addresses the synchronization problem of multi-agent systems with nonlinear controller outputs via event-triggered control, in which the combined edge state information is utilized, and all controller outputs are nonlinear to describe their inherent nonlinear characteristics and the effects of data transmission in digital communication networks. First, an edge-event-triggered policy is proposed to implement intermittent controller updates without Zeno behavior. Then, an edge-self-triggered solution is further investigated to achieve discontinuous monitoring of sensors. Compared with the previous event-triggered mechanisms, our policy design considers the controller output nonlinearities. Furthermore, the system's inherent nonlinear characteristics and networked data transmission effects are combined in a unified framework. Numerical simulations demonstrate the effectiveness of our theoretical results.
... When propagation delay changes exceeded a certain threshold, the bin-by-bin correction was launched, and LUT was updated on the threshold. Thus, the temperature correction scheme could be considered as an event-based control approach [22][23][24][25][26][27]. The proposed scheme was validated in a two-channel non-uniform multiphase (NUMP) TDC [28] using an Altera 60 nm Cyclone 10 LP FPGA and achieved a resolution of 8.8 ps root mean square (RMS) over a wide temperature range from 5 • C to 80 • C. ...
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... The decentralized decision-making of the EC paradigm toward generating accurate data must be energy efficient. For example, the continuous readings to provide high-resolution location can be energy inefficient in mobile devices prompting proposals to develop on-device cognitive-inspired control for power-aware human mobility analysis in IoT devices [97]. In addition to energy efficiency, the emerging EC, aiming to provide faster IoT operation and mobile devices, seeks to conserve and optimize memory, cache, server placement, size, weight, etc., calling for new studies and proposals. ...
It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.
... For example, the application of the ETC strategy in multi-agent system (MAS) equipped with microprocessors, such as physically distributed sensor/actuator networks [5], can lower the power consumption and prolong the lifetime of networks. Given these advantages, increasing attention has been paid to event-based control and communication [6][7][8][9][10][11][12][13]. Within these studies, the practices of ETC have been carried out on the general sampled-data systems [6], sensor systems [7-9], signal source localization and navigation of multiple robots [10][11][12], and human mobility analysis [13]. ...
... Given these advantages, increasing attention has been paid to event-based control and communication [6][7][8][9][10][11][12][13]. Within these studies, the practices of ETC have been carried out on the general sampled-data systems [6], sensor systems [7-9], signal source localization and navigation of multiple robots [10][11][12], and human mobility analysis [13]. ...
In this paper, a distributed event-triggered control strategy is proposed to investigate aflocking problem in a multi-agent system with Lipschitz nonlinear dynamics, where triggeringconditions are proposed to determine the instants to update the controller. A distributedevent-triggered control law with bounded action function is proposed for free flocking. It is provedthat the designed event-triggered controller ensures a group of agents reach stable flocking motionwhile preserving connectivity of the communication network. Lastly, simulations are provided toverify the effectiveness of the theoretical results.
The data mining on GNSS network RTK trajectories could be used to reveal the user behavior pattern, assess the local economy development and even improve the work plan. While the identification of the survey points by stationary and moving states information, is one of the most critical step of data preparation. The performance of traditional DBSCAN approach is not desired since it only supports the horizontal geographic distance information. In this paper an improved DBSCAN clustering is proposed. A novel set of parameters as the change of heights, number of satellites, fixing statues, are taken into consideration as well as the traditional ones as time interval, horizontal distance and minimal neighbors. After the clustering, the centroid analysis is applied to determine the survey points from each cluster. Both the simulated and real data experiments from HNCORS GNSS network RTK services are carried out to test the performance. The results show that the identification rate could reach 86.3%, which is increased by 15.2% compared to the traditional approach. Meanwhile, the best identification recall rate 90.6% occurs for road style surveying activities. Moreover, this approach could also be able to find the invalid or abnormal surveys.
The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies.
