Search

The motivations induced due to the presence of scale-free characteristics of neural systems governed by the well-known power-law distribution of neuronal activities have led to its convergence with the Internet of things (IoT) framework. The IoT is one such framework, where the self-organization of the connected devices is a momentous aspect. The devices involved in these networks inherently relate to the collection of several consolidated devices like the sensory devices, consumer appliances, wearables, and other associated applications, which facilitate a ubiquitous connectivity among the devices. This is one of the most significant prerequisites of IoT systems as several interconnected devices need to be included in the convolution for the uninterrupted execution of the services. Thus, in order to understand the scalability and the heterogeneity of these interconnected devices, the exponent of power-law plays a significant role. In this paper, an analytical framework to illustrate the ubiquitous power-law behavior of the IoT devices is derived. An emphasis regarding the mathematical insights for the characterization of the dynamic behavior of these devices is conceptualized. The observations made in this direction are illustrated through simulation results. Further, the traits of the wireless sensor networks, in context with the contemporary scale-free architecture, are discussed.

In today’s era, the exponential growth of data and its management is a matter of concern. Machine learning has shown its efficacy in multiple application areas. But machine learning on decentralized data was a hectic task since last decade. A novel technology has gained much importance in recent days i.e., federated learning which deals with training on decentralized and distributed data along with preservation of its privacy. Smartphone data being privacy-sensitive is used for locally training a global model which further is aggregated to generate an updated global model which again is distributed among multiple clients. This paper focuses on presenting the efficacy of federated learning by epitomizing an architecture showing the working mechanism of the technology. Further, this paper exhibits an intersection of on-device machine learning, privacy preservation technology and edge computing i.e., federated learning. Also, we have used TensorFlow Federated, an open source platform to simulate federated learning tasks for MNIST and extended MNIST (E-MNIST) datasets. Further, the results contain the loss and accuracy parameters for ten iterations repeated for six optimizer states (Opt st ) for each dataset. The peak accuracy that we achieved for MNIST and E-MNIST datasets are 0.843 and 0.853 respectively by using federated averaging algorithm. Further, the minimum loss value that we obtained for MNIST and E-MNIST datasets are 0.652 and 0.646 respectively. The execution time for implementing the algorithm for each dataset is presented in a graphical manner. Finally, certain application areas where federated learning technology has aided are scrutinized.

The advent of information communication technologies (ICTs) has contributed enormously to the expansion of healthcare amenities worldwide. The retrieval and delivery of healthcare information over digital platforms have increased scope for inter- and intracare facilities among the healthcare industry. This comprises exchanging hypersensitive information pertinent to the patients’ health records. Thus a highly risk-free approach is required to transmit the information over several remotely distributed platforms. In this chapter, we provide a secure architecture for acquisition and distribution of healthcare information (possibly acquired through sensory devices) over a large scenario to facilitate inter- and intracare health facilities to the patients. We address some of the pitfalls of existing healthcare services and provide schemes for bridging the inconsistencies observed between these services. We address the issues of electronic health records (EHRs) in adaptively handling the patients’ health details, thus providing more interoperability to physicians, caregivers, healthcare clinics, and insurance agencies. We also provide an in-depth discussion regarding several enabling technologies that have led to the success of these smart healthcare services. Through our study, we introduce a blockchain-based architecture, which can address some of the critical care services by adding more ubiquity, reliability, and traceability to healthcare sector that conventional healthcare services failed to achieve.

With the outbreak of COVID-19 pandemic, several educational and business organizations have adopted online video conferencing platforms to facilitate their smooth functioning. These platforms have largely favored educational institutions for remotely conducting virtual classes over various electronic learning (e-learning) platforms. This has imposed challenges on existing Internet platforms for efficiently managing network performance generated from the extensive use of these platforms. In this paper, a framework for monitoring the effect of different network parameters, while using some well-known e-learning platforms is provided. The different LTE network performance parameters like uplink speed, downlink speed, network latency, and jitter are considered. We observed that the LTE network performance parameters obtained while using Microsoft Teams is optimum. Since, we observed that Microsoft Teams provided an optimal performance, we further perform analysis on Microsoft Teams. We further obtain the empirical distribution pertaining to the LTE network communication features by employing Gamma distribution. From our analysis, it is observed that the network throughput, latency, and jitter can be optimized to enhance the users' quality of experience by employing the proposed strategy.

A complex issue in handling systems with continually changing processing demands is an intractable task. A more current example of these systems can be observed in wireless sensor networks and traffic-intensive IoT networks. Thus, an adaptive framework is desired which can handle the load and can also assist in enhancing the performance of the system. In this paper, our objective is to provide the non-stationary solution of Erlang loss queueing model where s servers can serve at most s jobs at a time. We have employed time-dependent perturbation theory to obtain the probability distribution of M/M/s/s queueing model. The time-dependent arrival and service rates are assumed to be in sinusoidal form. The opted theory gives approximation for probability distribution correct up to first and second order. The result shows that first- and second-order approximations provide better approximation than the existing ones.

The extensive growth in popularity of Internet of Things (IoT) has led to the generation of massive amount of data from several heterogeneous sensory devices. This has also led to the increase in energy consumption by these connected devices. Smart buildings are one such platform which are equipped with several micro-controllers and sensors, generating a huge amount of redundant information at their data acquisition level. As a result, real-time applications may not be efficiently executed due to latency delays at the cloud service end. This requires several devices at cloud service end to execute the massive amount of data generated by these sensors, which does not satisfy green computing criteria. In this context, a novel local processing mechanism (LPM) is proposed, which favors an improved IoT service architecture for smart buildings. From the perspective of green computing, the proposed LPM framework facilitates reduction of manifolds at data acquisition level of sensor nodes. This paper also addresses the concept of optimal use of sensors in a wireless sensor network (WSN) and estimates costs corresponding to non-Poisson and Poisson arrival of data packets at local processor using the well-known queuing model. We also provide an efficient algorithm for smart buildings using our expert Markov switching (EMS) model, which is a well known probabilistic model in the field of artificial intelligence (AI) for subjectively validating real sensory data sets (viz., temperature, pressure, and humidity). Further, it has been analyzed that the proposed EMS algorithm outperforms several other algorithms conventionally used for determining the state of large-scale dynamic sensor networks. The service cost of proposed model has been compared with conventional model under various stress conditions viz., arrival rate, service rate, and number of clusters. It is observed that the proposed model operates well by leveraging green computing criteria. Thus, in the aforementioned context, this paper provides thing-centric, data-centric, and service-oriented IoT architecture.

The massive technological expansions in modern healthcare solutions have substantially influenced the development of several unfolding research fields. One such interesting area involves the therapeutic prognosis of pathological conditions in patients by using knowledge engineering techniques. These techniques have inevitably assisted in the growth of healthcare systems. In this paper, we provide a comprehensive classification framework for identification of pathological disorders in children. We consider a real dataset to study the accuracy in prediction of different classification algorithms for the identification of seven different pathological conditions. We then present an experimental analysis corresponding to the performance of each algorithm. This framework can be used for the early detection of diseases and for suggesting appropriate precautionary measures. Further a more stratified understanding of the demographics of a patient’s pathological conditions can be determined and appropriate treatment can be recommended.

The advent of geographic information system has proven its efficiency in many relevant domains but still needs some more explorations. In this paper, a novel architecture towards the convergence of machine learning and GIS technology is presented. The enabling technologies associated to the implementations performed are well-defined and presented in this paper. Further, relevant works in the domain of the merger technology of machine learning and GIS technology is epitomized. This paper considered three relevant datasets viz., Indian census data, Zomato restaurants data and COVID-19 data for India for practically realizing the proposed framework. The proficiency of the proposed framework is presented through various results generated in this paper. Furthermore, the limitations posed by the proposed framework and ways to tackle the challenges are presented. Various geospatial visualization operations along with statistical plots are shown in this paper to epitomize the overlay analysis, point-cluster generation, heatmap analysis and spatial clustering approaches. Further, wherever required a Microsoft Bing map is provided to epitomize the performed risk analysis along with zonal classification. Finally, concluding remarks are summarized with the presentation of recent techniques like deep learning, serverless computing paradigm and so on to improve the proposed framework with diminished limitations.

The ability of the Internet of things (IoT) to incorporate anything and everything has induced and it is revolutionary applications in spheres of smart healthcare, smart living, smart cities, smart governance, and many more. A more general illustration for the IoT-based administration is the smart waste monitoring and management scheme for the smart cities. The smart waste management comprises of certain information and communication technologies (ICT) which support the tracking and management of the garbage bins. In this paper, we present a strategy for the garbage bin detection problem based on the thresholding scheme and also present a real-time waste management algorithm for the dynamic selection of optimal paths by the garbage collection vans. We also provide an optimal cost model subject to the threshold-based constraints which falls under the time complexity \(O\left( {p + n\;\log \;n} \right)\), (where \(p\) and \(n\) denote the path and the location of the smart dustbins), for our proposed algorithm.