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CALL FOR BOOK CHAPTERS INTELLIGENT HEALTHCARE SYSTEMS Scopus-indexed, Free of charges, published by CRC Press/Taylor & Francis Keywords: Health 4.0, Internet of Medical Things, Public Health, Disaster Mitigation, Vehicular Communications, Wireless Networks, Wearables, Artificial Intelligence in Healthcare, Cyber-Physical Systems, Smart Designs, Blockchain, Digital Twins, Telemedicine, 5G, Medical Imaging, Cloud Computing. Editor: Vania V. Estrela vania.estrela.phd@ieee.org Federal Fluminense University, RJ, Brazil Book Content Description Information is paramount to the healthcare sector, entailing intense data, medical epidemiologic sets, Internet browsing records, surveys, complex engineering models, and so on via the Cloud. This quest for knowledge prompts the data dimensionality, which calls for more sophisticated and efficient information strategies. Health science and biology are very complex fields fully embedded in information technology, but the associated processes are much too intricate to be faithfully modeled. It is not easy to extract knowledge starting from raw data, and it is also expensive. Artificial intelligence (AI) in healthcare (AIH) has been the primary concern to develop expert systems aimed for diagnostic and decision-making in knowledge acquisition, representation, reasoning, and explanation. Many healthcare facilities (HFs) have data acquisition, monitoring, and storage systems integrated into larger-scale information systems. This vast amount of information and databases stemming from medical applications cause hinder analysis and decision making. Hence, there is a need to develop better tools for accessing/storing/analyzing knowledge while effectively using multimodal data. These necessities become essential in the healthcare realm as decision-making relies on knowledge from multidisciplinary areas. This book intends to provide computational methods for intelligent health data analysis to narrow the gap between data gathering and data comprehension with applications in medicine, health care, biology, pharmacology, and related areas. Intelligent Data Analysis (IDA) expedites healthcare analyses and applications. IDA employs specialized statistical, pattern recognition, machine learning (ML), data abstraction, and visualization tools for analysis of data and discovery of mechanisms that created them. Healthcare data typically involve many records/variables, subtle interactions between entities, or a combination of all factors. Engineering, computing science, and ML empower data analysis tasks. The IDA extracts knowledge from too much data, with a vast amount of variables, data that represents very complex, nonlinear, real-life problems. IDA can help raw data analysis, coping with prediction tasks without knowing the theoretical description of the underlying process, classification tasks of new events, or modeling unknown processes. Classification, prediction, and modeling are the cornerstones brought in by IDA. This book focuses on AIH methods and tools to bridge data gathering and data comprehension. Emphasis will also be given to problem solving within HFs to handle patient records, data warehousing, intelligent alarming, competent monitoring, etc. In medicine, overcoming this gap is particularly crucial since medical decision-making needs comprehension of healthcare data regularities and trends. This book tackles different IDA approaches. Submission Schedule One-page abstract submission: May 20, 2021 Structured Abstract Template in Word Manuscript submission due: May 31, 2021 Manuscript Template in Word Manuscript Template in pdf Review notification with acceptance/rejection: June 30, 2021 Revised paper submission: July 31, 2021 Camera-Ready Submission: August 31, 2021 &nbsp

Previous work has demonstrated the feasibility of Bluetooth Low Energy (BLE) as an alternative technology for data transfers in inter-vehicular communication (IVC) scenarios. Bluetooth 5.x core specifications enhance the trade-off between energy requirements, communication range and flexibility. In this paper, we aim to analyse the potential of Bluetooth 5 features for VANET applications, proposing a connectionless communication system. By means of field experiments, we evaluate long range and 2 × speed features, defining a set of communication scenarios. This allows us to test both Bluetooth 5.x range and application throughput. The evaluation includes experiments of V2I communications carried out under real highway traffic conditions. The experiments conducted demonstrate that communication ranges up to 300 m may be achieved depending on the communications scenario. The results also show how throughput degrades as the distance between devices increases. The results obtained are used to discuss future work, aimed at deeper analysing Bluetooth 5 features for VANET applications, completing the development of our prototype and evaluating VANET connectionless communications with the features included in the latest Bluetooth 5.2 specification.

The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network’s robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure.

In recent years, low-power wide-area network (LPWAN) has received widespread popularity with long-range and wide-area communication at low power for the Internet of Things (IoT) systems. Among many vendors of LPWAN, long-range low-power wireless communications, also called LoRa, is one of the competing standards and is well known in both academia and industrial communities as an emerging research area. Among the LoRa applications, unmanned aerial vehicles (UAV) systems are emerging with the benefits of extended battery life and a long communication range. In this paper, we investigate the network capacity with the mixture of concurrent and sequential transmission (MCST) scheme over LoRa networks. From the simulation results, it can be seen that MCST is suitable for implementation in the LoRa network. Specifically, MCST can achieve higher throughput with low transmission latency and energy consumption compared to the existing CSMA approach LoRa MAC. Besides, we also propose a modified MCST over the LoRa (mMCST/LoRa) scheme to mitigate the transmission latency further. The simulation results reveal a better performance in terms of throughput, latency and energy consumption, regardless of the frame payload size and the number of nodes in the network.

Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). A detection node (i.e., collector) should discover available routes to a collection node for data collection and collect security-related data during route discovery for determining reliable routes. However, few studies provide incentives for security-related data collection in MANETs. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of Route REQuest (RREQ) forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and Route REPlies, in short RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.

Mobile ad hoc networks (MANETs) are now key in today’s new world. They are critically needed in many situations when it is crucial to form a network on the fly while not having the luxury of time or resources to configure devices, build infrastructure, or even have human interventions. Ad hoc networks have many applications. For instance, they can be used in battlefields, education, rescue missions, and many other applications. Such networks are characterized by high mobility, low resources of power, storage, and processing. They are infrastructure-less; this means that they don’t use infrastructure equipment for communication. These networks rely instead on each other for routing and communication. MANETs use a hopping mechanism where each node in a network finds another node within its communication range and use it as a hop for delivering the message through another node and so on. In standard networks, there is dedicated equipment for specific functions such as routers, servers, firewalls, etc., while in ad hoc networks, every node performs multiple functions. For example, the routing function is performed by nodes. Hence, they are more vulnerable to attacks than standard networks. The main goal of this paper is to propose a solution for detecting black hole attacks using anomaly detection based on a support vector machine (SVM). This detection system aims at analyzing the traffic of the network and identifying anomalies by checking node behaviors. In the case of black hole attacks, the attacking nodes have some behavioral characteristics that are different from normal nodes. These characteristics can be effectively detected using our lightweight detection system. To experiment with the effectiveness of this solution, an OMNET++ simulator is used to generate traffic under a black hole attack. The traffic is then classified into malicious and non-malicious based on which the malicious node is identified. The results of the proposed solution showed very high accuracy in detecting black hole attacks.

This paper presents a solution for building awareness of the electromagnetic situation in cognitive mobile ad hoc networks (MANET) using the cooperative spectrum sensing method. Signal detection is performed using energy detectors with noise level estimation. Based on the evidence theory, the fusion center decides on the particular channel occupancy, which can process incomplete and unambiguous input data. Next, a reinforced machine learning algorithm estimates the usefulness of particular channels for the MANET transmission and creates backup channels list that could be used in case of interferences. Initial simulations were performed using the MATLAB environment, and next an OMNET-based MAENA high fidelity simulator was used. Performed simulations showed a significant increase in sensing efficiency compared to sensing performed using simple data fusion rules.

Delay-tolerant networks (DTNs) are networks where there is no immediate connection between the source and the destination. Instead, nodes in these networks use a store–carry–forward method to route traffic. However, approaches that rely on flooding the network with unlimited copies of messages may not be effective if network resources are limited. On the other hand, quota-based approaches are more resource-efficient but can have low delivery rates and high delivery delays. This paper introduces the Enhanced Message Replication Technique (EMRT), which dynamically adjusts the number of message replicas based on a node’s ability to quickly disseminate the message. This decision is based on factors such as current connections, encounter history, buffer size history, time-to-live values, and energy. The EMRT is applied to three different quota-based protocols: Spray and Wait, Encounter-Based Routing (EBR), and the Destination-Based Routing Protocol (DBRP). The simulation results show that applying the EMRT to these protocols improves the delivery ratio, overhead ratio, and latency average. For example, when combined with Spray and Wait, EBR, and DBRP, the delivery probability is improved by 13%, 8%, and 10%, respectively, while the latency average is reduced by 51%, 14%, and 13%, respectively.

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.

International audience; In this paper, we investigate and present how to generate application traces of IoT (Internet of Things) Applications in an automated, repeatable and reproducible manner. By using the FIT IoT-Lab large scale testbed and relying on state-of-the-art software engineering techniques, we are able to produce, collect and share artifacts and datasets in an automated way. This makes it easy to track the impact of software updates or changes in the radio environment both on a small scale, e.g. during a single day, and on a large scale, e.g. during several weeks. By providing both the source code for the trace generation as well as the resulting datasets, we hope to reduce the learning curve to develop such applications and encourage reusability as well as pave the way for the replication of our results. While we focus in this work on IoT networks, we believe such an approach could be of used in many other networking domains.

Mobile Ad hoc Networks (MANETs) are intended to work without a fixed framework and provide dependable interchanges to ground vehicles, boats, airplanes, or people and structure a self-mending process that will empower persistent correspondences in any event, when at least one of its nodes are debilitated or briefly expelled from the system. Notwithstanding, MANETs demonstrate themselves to be progressively harder to create for enormous systems with hundreds or thousands more nodes than initially envisioned. In our proposed technique, the node switches its communication mode depending on the connectivity of the adjacent nodes. The transmission power of each node will be calculated with the help of two major scenarios i.e., tree scenario and zone scenario. The autonomous clustering of the nodes among the tree and the zone scenario will be channelized by a comparison of the transmission power (residual energy) among the nodes. The inter and the intra communication of the node is also discussed in the paper. The result will be carried out by the simulation work in various perspectives, such as checking the percentage level of malicious nodes, traffic density, transmission power, and the longevity of nodes.