Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

High mobility group box 1 in the central nervous system:regeneration hidden beneath inflammation

High-mobility group box 1 was first discovered in the calf thymus as a DNA-binding nuclear protein and has been widely studied in diverse fields,including neurology and neuroscience.High-mobility group box 1 in the extracellular space functions as a pro-inflammatory damage-associated molecular pattern,which has been proven to play an important role in a wide variety of central nervous system disorders such as ischemic stroke,Alzheimer’s disease,frontotemporal dementia,Parkinson’s disease,multiple sclerosis,epilepsy,and traumatic brain injury.Several drugs that inhibit high-mobility group box 1 as a damage-associated molecular pattern,such as glycyrrhizin,ethyl pyruvate,and neutralizing anti-high-mobility group box 1 antibodies,are commonly used to target high-mobility group box 1 activity in central nervous system disorders.Although it is commonly known for its detrimental inflammatory effect,high-mobility group box 1 has also been shown to have beneficial pro-regenerative roles in central nervous system disorders.In this narrative review,we provide a brief summary of the history of high-mobility group box 1 research and its characterization as a damage-associated molecular pattern,its downstream receptors,and intracellular signaling pathways,how high-mobility group box 1 exerts the repair-favoring roles in general and in the central nervous system,and clues on how to differentiate the pro-regenerative from the pro-inflammatory role.Research targeting high-mobility group box 1 in the central nervous system may benefit from differentiating between the two functions rather than overall suppression of high-mobility group box 1.

LiMo 2040 Sustainable urban Living and Mobility Concept-Elevator System

How can individual mobility in urban areas be maintained alongside scooters and cargo bikes if conventional vehicles are foreseeably no longer allowed to enter city centers?And how can urban living be combined with individual mobility in a sustainable and socially acceptable way?LiMo-2040 attempts to provide answers to these questions.It follows a holistic approach according to the criteria:As light,as compact and as simple(cost-effective)as possible.Modular e-vehicle concepts(consisting of vehicle cabin and chassis)are known,but have not yet been thought through to their logical conclusion.The LiMo cabin is not only a vehicle cabin,but also a component of a modern high-rise apartment.It therefore requires no parking space and combines urban living and individual mobility sustainably and cost-effectively.If a vehicle is needed,an app can be used to book a chassis that comes along autonomously and waits until the cabin,including its occupants,travels down a sophisticated rail system,docks and autonomously heads for the desired destination.

The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.

Sustainable Urban Mobility: (A)Synchrony between Subway Systems

The present study analyses the differences between the subways systems of two cities, Lisbon (Portugal) and Brasília (Brazil), verifying the extent to which their subway systems are spatially integrated with the respective urban fabric-which would promote a better synchrony in terms of economy and sustainability. For interpreting the data, the methodology and tools of the Theory of the Social Logic of Space were used, by means of axial maps, researching the relationship between the constructed and empty (public spaces) areas of the urban structures. Based on the findings, meaningful differences between the subway systems and the configuration of urban spaces were observed, as a product of specific design matrixes. In Lisbon, the mobility associated to the subway seems to be encouraged by the integration of the system with the potential for movement provided by the urban tissue (making the secondary centralities articulated with the subway stations more dynamic). In Brasilia, however, there are several difficulties for such mobility, due to the predominant role of the empty spaces in the city which weakens the gathering potential of such areas, despite the fact that such articulation with secondary centralities is also present there.

Effectiveness of exercise in hepatic fat mobilization in nonalcoholic fatty liver disease: Systematic review

AIM: To investigate the efficacy of exercise interventions on hepatic fat mobilization in non-alcoholic fatty liver disease(NAFLD) patients.METHODS: Ovid-Medline, Pub Med, EMBASE and Cochrane database were searched for randomized trials and prospective cohort studies in adults aged ≥ 18 which investigated the effects of at least 8 wk of exercise only or combination with diet on NAFLD from 2010 to 2016. The search terms used to identify articles, in which exercise was clearly described by type, duration, intensity and frequency were: "NASH", "NAFLD", "nonalcoholic steatohepatitis", "non-alcoholic fatty liver disease", "fat", "steatosis", "diet", "exercise", "MR spectroscopy" and "liver biopsy". NAFLD diagnosis, as well as the outcome measures, was confirmed by either hydrogen-magnetic resonance spectroscopy(H-MRS) or biopsy. Trials that included dietary interventions along with exercise were accepted if they met all criteria. RESULTS: Eight studies met selection criteria(6 with exercise only, 2 with diet and exercise with a total of 433 adult participants). Training interventions ranged between 8 and 48 wk in duration with a prescribed exercise frequency of 3 to 7 d per week, at intensities between 45% and 75% of VO2 peak. The most commonly used imaging modality was H-MRS and one study utilized biopsy. The effect of intervention on fat mobilization was 30.2% in the exercise only group and 49.8% in diet and exercise group. There was no difference between aerobic and resistance exercise intervention, although only one study compared thetwo interventions. The beneficial effects of exercise on intrahepatic triglyceride(IHTG) were seen even in the absence of significant weight loss. Although combining an exercise program with dietary interventions augmented the reduction in IHTG, as well as improved measures of glucose control and/or insulin sensitivity, exercise only significantly decreased hepatic lipid contents.CONCLUSION: Prescribed exercise in subjects with NAFLD reduces IHTG independent of dietary intervention. Diet and exercise was more effective than exercise alone in reducing IHTG.

Effectiveness of Maitland and Mulligan mobilization methods for adults with knee osteoarthritis:A systematic review and metaanalysis

BACKGROUND As a serious global problem,knee osteoarthritis(KOA)often leads to pain and disability.Manual therapy is widely used as a kind of physical treatment for KOA.AIM To explore further the efficacy of Maitland and Mulligan mobilization methods for adults with KOA.METHODS We searched PubMed,the Cochrane Library,EMbase,Web of Science and Google Scholar from inception to September 20,2020 to collect studies comparing Maitland and Mulligan mobilization methods in adults with KOA.The quality of the studies was assessed using the Physiotherapy Evidence Database Scale for randomized controlled trials.Data analyses were performed using Review Manager 5.0 software.RESULTS A total of 341 articles were screened from five electronic databases(PubMed,the Cochrane Library,EMbase,Web of Science and Google Scholar)after excluding duplicates.Ultimately,eight trials involving 471 subjects were included in present systematic review and meta-analysis.The mean PEDro scale score was 6.6.Mulligan mobilization was more effective in alleviating pain[standardized mean difference(SMD)=0.60;95%confidence interval(CI):0.17 to 1.03,P=0.007;I2=60%,P=0.020]and improving Western Ontario and McMaster Universities function score(SMD=7.41;95%CI:2.36 to 12.47,P=0.004;I2=92%,P=0.000).There was no difference in the effect of the two kinds of mobilization on improving the range of motion(SMD=9.63;95%CI:-1.23 to 20.48,P=0.080;I2=97%,P=0.000).CONCLUSION Mulligan mobilization technique is a promising intervention in alleviating pain and improving function score in KOA patients.

Early mobilization for mechanically ventilated patients in the intensive care unit:a systematic review and meta-analysis

Background: Early mobilization(EM) is a regimen that was carried out by physiotherapists in a relatively early stage. It has been investigated by an increasing number of researchers. However, there has not been a meta-analysis concerning whether EM could benefit the clinical outcomes of critically ill patients requiring mechanical ventilation(MV). The present systematic review aims to evaluate the effect of EM compared with immobilization for mechanically ventilated patients.Methods: A computerized literature search was performed in six databases for related articles from inception to June 2017. We included randomized controlled trials and controlled clinical trials and used the Physiotherapy Evidence Database scale to assess the quality of included studies. Primary outcomes were measures of muscle function, duration of MV, and incidence of mortality.Secondary outcomes were adverse effects and length of stay(LOS) in intensive care unit(ICU) and hospital.Results: Eight trials were included; of those, only one study without standard EM reported that the intervention was invalid to improve the outcomes. The result of meta-analysis indicated that EM shortened the duration of MV; however, it had no positive effect on mortality and LOS in ICU.Conclusions: This review suggests that EM improves the muscle function and ventilation duration. Further research highlighting standard intervention and specific groups is needed.

Systematic evaluation and trial sequential analysis of warming acupuncture combined with joint mobilization in the treatment of scapulohumeral periarthritis

Objective:To evaluate the clinical efficacy of warming acupuncture combined with joint mobilization for the treatment of scapulohumeral periarthritis.Methods:A search for published randomized controlled trials(RCT)investigating warming acupuncture combined with joint mobilization for the treatment of scapulohumeral periarthritis was performed using the Cochrane Library,PubMed,Embase,and Web of Science databases.According to requirements of the Cochrane systematic review,all evaluations of RCT investigating warming acupuncture with joint mobilization in the treatment of scapulohumeral periarthritis were performed via method quality assessment,data extraction,and data analysis.Revman 5.3 and Stata 12.0 statistical software were used for the meta-analysis,and the trial sequential analysis(TSA)software estimated the required information size for each outcome.Results:A total of 14 articles were retrieved for meta-analysis,which included 551 cases in the treatment group and 539 cases in the control group.Meta-analysis showed that:(1)warming acupuncture combined with joint mobilization improved total efficiency[OR=6.16,95%CI(3.79,10.00),Z=7.34;P<0.001),TSA results confirmed the results of the meta-analysis;(2)warming acupuncture combined with joint mobilization improved the cure rate[OR=2.84,95%CI(2.19,3.70),P<0.001],TSA results revealed that no further tests were needed to verify;(3)warming acupuncture combined with joint mobilization reduced the number of treatments required for healing[MD=-7.49,95%CI(-9.75,-5.23),P<0.001],TSA results confirmed the results of the meta-analysis;(4)in comparing visual analog scale scores before and after treatment,meta-analysis result showed that:SMD=-2.01,95%CI(-2.37,-1.65),P<0.001,the difference was statistically significant,TSA results confirmed the results of the meta-analysis.Conclusion:Warming acupuncture combined with joint mobilization had a significant effect on the treatment of scapulohumeral periarthritis.

Instrumented assisted soft tissue mobilization vs extracorporeal shock wave therapy in treatment of myofascial pain syndrome

BACKGROUND Active myofascial trigger points(TrPs)often occur in the upper region of the upper trapezius(UT)muscle.These TrPs can be a significant source of neck,shoulder,and upper back pain and headaches.These TrPs and their related pain and disability can adversely affect an individual’s everyday routine functioning,work-related productivity,and general quality of life.AIM To investigate the effects of instrument assisted soft tissue mobilization(IASTM)vs extracorporeal shock wave therapy(ESWT)on the TrPs of the UT muscle.METHODS A randomized,single-blind,comparative clinical study was conducted at the Medical Center of the Egyptian Railway Station in Cairo.Forty patients(28 females and 12 males),aged between 20-years-old and 40-years-old,with active myofascial TrPs in the UT muscle were randomly assigned to two equal groups(A and B).Group A received IASTM,while group B received ESWT.Each group was treated twice weekly for 2 weeks.Both groups received muscle energy technique for the UT muscle.Patients were evaluated twice(pre-and posttreatment)for pain intensity using the visual analogue scale and for pain pressure threshold(PPT)using a pressure algometer.RESULTS Comparing the pre-and post-treatment mean values for all variables for group A,there were significant differences in pain intensity for TrP1 and TrP2(P=0.0001)and PPT for TrP1(P=0.0002)and TrP2(P=0.0001).Also,for group B,there were significant differences between the pre-and post-treatment pain intensity for TrP1 and TrP2 and PPT for TrP1 and TrP2(P=0.0001).There were no significant differences between the two groups in the post-treatment mean values of pain intensity for TrP1(P=0.9)and TrP2(P=0.76)and PPT for TrP1(P=0.09)and for TrP2(P=0.91).CONCLUSION IASTM and ESWT are effective methods for improving pain and PPT in patients with UT muscle TrPs.There is no significant difference between either treatment method.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration.However,due to the particularity of its parallel structure,the effective utilization rate of energy is not high,which has become an important obstacle to its practical application.To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability.Based on the configuration and walking modes of robot,the mathematical model of the power consumption of mobile system is set up.In view of the tripod gait is often selected for the six-legged robots,the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot’s statically indeterminate problem and constructing the equal force distribution.Then,the power consumption of robot mobile system is solved under different working conditions.The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint,body height,and span.The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption.According to the walking experiments of prototype,the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio,body height,and span.Then,the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system.The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

Task-Oriented Topology System Synthesis of Reconfigurable Legged Mobile Lander Integrating Active and Passive Metamorphoses

To explore hostile extraterrestrial landforms and construct an engineering prototype,this paper presents the task-oriented topology system synthesis of reconfigurable legged mobile lander(ReLML)with three operation modes from adjusting,landing,to roving.Compared with our preceding works,the adjusting mode with three rotations(3R)provides a totally novel exploration approach to geometrically matching and securely arriving at complex terrains dangerous to visit currently;the landing mode is redefined by two rotations one translation(2R1T),identical with the tried-and-tested Apollo and Chang'E landers to enhance survivability via reasonable touchdown buffering motion;roving mode also utilizes 2R1T motion for good motion and force properties.The reconfigurable mechanism theory is first brought into synthesizing legged mobile lander integrating active and passive metamorphoses,composed of two types of metamorphic joints and metamorphic execution and transmission mechanisms.To reveal metamorphic principles with multiple finite motions,the finite screw theory is developed to present the procedure from unified mathematical representation,modes and source phase derivations,metamorphic joint and limb design,to final structure assembly.To identify the prototype topology,the 3D optimal selection matrix method is proposed considering three operation modes,five evaluation criteria,and two topological subsystems.Finally,simulation verifies the whole task implementation process to ensure the reasonability of design.

Multi-Objective Optimization for NOMA-Based Mobile Edge Computing Offloading by Maximizing System Utility

Mobile Edge Computing(MEC)is a technology for the fifth-generation(5G)wireless communications to enable User Equipment(UE)to offload tasks to servers deployed at the edge of network.However,taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and contradictory.Non-orthogonal multiple access(NOMA)enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G,which makes the problem even more complex in the NOMA-MEC system.In this work,a system utility maximization model is present to NOMA-MEC system,and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation,SIC order,transmission time slot allocation,which can easily achieve a better trade-off between the delay and energy consumption.The simulation results prove that the proposed method is effective for NOMA-MEC systems.

Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence

BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.

Coordinated restoration optimization of power-gas integrated energy system with mobile emergency sources

In an integrated energy system(IES) composed of multiple subsystems, energy coupling causes an energy supply blockage or shutdown in one subsystem, thereby affecting the energy flow distribution optimization of other subsystems.The energy supply should be globally optimized during the IES energy supply restoration process to produce the highest restoration net income. Mobile emergency sources can be quickly and flexibly connected to supply energy after an energy outage to ensure a reliable supply to the system, which adds complexity to the decision. This study focuses on a powergas IES with mobile emergency sources and analyzes the coupling relationship between the gas distribution system and the power distribution system in terms of sources, networks, and loads, and the influence of mobile emergency source transportation. The influence of the transient process caused by the restoration operation of the gas distribution system on the power distribution system is also discussed. An optimization model for power-gas IES restoration was established with the objective of maximizing the net income. The coordinated restoration optimization decision-making process was also built to realize the decoupling iteration of the power-gas IES, including system status recognition, mobile emergency source dispatching optimization, gas-to-power gas flow optimization, and parallel intra-partition restoration scheme optimization for both the power and gas distribution systems. A simulation test power-gas IES consisting of an 81-node medium-voltage power distribution network, an 89-node medium-pressure gas distribution network, and four mobile emergency sources was constructed. The simulation analysis verified the efficiency of the proposed coordinated restoration optimization method.

Adaptive Graph Convolutional Recurrent Neural Networks for System-Level Mobile Traffic Forecasting

Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.

Use of new technologies for the promotion of physical activity in patients with mental illness:A systematic review

BACKGROUND Physical exercise is an underutilized tool for the management of mental disorders.New technologies have made a breakthrough in health care,and one of its possible applications(apps)could be that of customizing exercise programs for special populations,such as patients with mental disorders.However,the app of the so-called e-health to mental health care is still limited.AIM To know the efficacy of apps to promote physical activity in patients with mental disorders.METHODS We conducted a systematic review of the PubMed and Embase databases with the aim of exploring the use of new technologies for the enhancement of physical exercise in patients with a psychiatric illness.Following the selection process,10 articles were included in the review.RESULTS The most commonly used devices in this type of intervention are wearable devices and web platforms.Good results in terms of effectiveness and acceptability were obtained in most of the studies.CONCLUSION Our findings suggest that the use of new technologies in mental health represents a feasible strategy with great potential in clinical practice.

An Efficient Memory Management for Mobile Operating Systems Based on Prediction of Relaunch Distance

Recently,various mobile apps have included more features to improve user convenience.Mobile operating systems load as many apps into memory for faster app launching and execution.The least recently used(LRU)-based termination of cached apps is a widely adopted approach when free space of the main memory is running low.However,the LRUbased cached app termination does not distinguish between frequently or infrequently used apps.The app launch performance degrades if LRU terminates frequently used apps.Recent studies have suggested the potential of using users’app usage patterns to predict the next app launch and address the limitations of the current least recently used(LRU)approach.However,existing methods only focus on predicting the probability of the next launch and do not consider how soon the app will launch again.In this paper,we present a new approach for predicting future app launches by utilizing the relaunch distance.We define the relaunch distance as the interval between two consecutive launches of an app and propose a memory management based on app relaunch prediction(M2ARP).M2ARP utilizes past app usage patterns to predict the relaunch distance.It uses the predicted relaunch distance to determine which apps are least likely to be launched soon and terminate them to improve the efficiency of the main memory.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).