Chapter

A Decentralized Model for IoT Networks

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Telemedicine over Internet of Things (IoT) generates an unprecedented amount of data, which further requires transmission, analysis, and storage. Deploying cloud computing to handle data of this magnitude will introduce unacceptable data analysis latency and high storage costs. Thus, mobile edge computing (MEC) deployed between the cloud and users, which is close to the nodes of data generation, can tackle these problems in 5G scenarios with the help of artificial intelligence. This paper proposes a telemedicine system based on MEC and artificial intelligence for remote health monitoring and automatic disease diagnosis. The integration of different technologies such as computers, medicine, and telecommunications will significantly improve the efficiency of patient treatment and reduce the cost of health care.
Article
Full-text available
Smart cities solutions are often monolithically implemented, from sensors data handling through to the provided services. The same challenges are regularly faced by different developers, for every new solution in a new city. Expertise and know-how can be re-used and the effort shared. In this article we present the methodologies to minimize the efforts of implementing new smart city solutions and maximizing the sharing of components. The final target is to have a live technical community of smart city application developers. The results of this activity comes from the implementation of 35 city services in 27 cities between Europe and South Korea. To share efforts, we encourage developers to devise applications using a modular approach. Single-function components that are re-usable by other city services are packaged and published as standalone components, named Atomic Services. We identify 15 atomic services addressing smart city challenges in data analytics, data evaluation, data integration, data validation, and visualization. 38 instances of the atomic services are already operational in several smart city services. We detail in this article, as atomic service examples, some data predictor components. Furthermore, we describe real-world atomic services usage in the scenarios of Santander and three Danish cities. The resulting atomic services also generate a side market for smart city solutions, allowing expertise and know-how to be re-used by different stakeholders.
Article
Full-text available
Driverless cars with artificial intelligence (AI) and automated supermarkets run by collaborative robots (cobots) working without human supervision have sparked off new debates: what will be the impacts of extreme automation, turbocharged by the Internet of Things (IoT), AI, and the Industry 4.0, on Big Data and omics implementation science? The IoT builds on (1) broadband wireless internet connectivity, (2) miniaturized sensors embedded in animate and inanimate objects ranging from the house cat to the milk carton in your smart fridge, and (3) AI and cobots making sense of Big Data collected by sensors. Industry 4.0 is a high-tech strategy for manufacturing automation that employs the IoT, thus creating the Smart Factory. Extreme automation until "everything is connected to everything else" poses, however, vulnerabilities that have been little considered to date. First, highly integrated systems are vulnerable to systemic risks such as total network collapse in the event of failure of one of its parts, for example, by hacking or Internet viruses that can fully invade integrated systems. Second, extreme connectivity creates new social and political power structures. If left unchecked, they might lead to authoritarian governance by one person in total control of network power, directly or through her/his connected surrogates. We propose in this study, Industry 5.0 that can democratize knowledge coproduction from Big Data, building on the new concept of symmetrical innovation. Industry 5.0 utilizes IoT, but differs from predecessor automation systems by having three-dimensional (3D) symmetry in innovation ecosystem design: (1) a built-in safe exit strategy in case of demise of hyperconnected entrenched digital knowledge networks. Importantly, such safe exists are orthogonal-in that they allow "digital detox" by employing pathways unrelated/unaffected by automated networks, for example, electronic patient records versus material/article trails on vital medical information; (2) equal emphasis on both acceleration and deceleration of innovation if diminishing returns become apparent; and (3) next generation social science and humanities (SSH) research for global governance of emerging technologies: "Post-ELSI Technology Evaluation Research" (PETER). Importantly, PETER considers the technology opportunity costs, ethics, ethics-of-ethics, framings (epistemology), independence, and reflexivity of SSH research in technology policymaking. Industry 5.0 is poised to harness extreme automation and Big Data with safety, innovative technology policy, and responsible implementation science, enabled by 3D symmetry in innovation ecosystem design.
Conference Paper
Full-text available
Internet of Things (IoT) is an emerging technology that is making our world smarter.
Article
Full-text available
In the next-generation technology, Internet of Things (IoT), billions of smart objects will communicate with one another to make human lives more convenient. IoT is based on wireless sensor network (WSN), and Zigbee is one of the most popular WSN protocols. A mature IoT environment involves heavy WSN data transmission causing bottleneck problems. However, Zigbee’s AODV routing stack does not have load balance mechanism to handle bursty traffic. Therefore, we develop Multipath Load Balancing (MLB) Routing to substitute Zigbee’s AODV routing. Our proposed MLB consists of two main designs: LAYER_DESIGN and LOAD_BALANCE. LAYER_DESIGN assigns nodes into different layers based on node distance to IoT gateway. Nodes can have multiple next-hops delivering IoT data. All neighboring layer nodes exchange flow information containing current load, used by LOAD_BALANCE to estimate future load of next-hops. With MLB, nodes can choose the neighbors with the least load as the next-hops and thus can achieve load balance and avoid bottlenecks. Compared with Zigbee’s AODV and multipath version AODV (AOMDV), experiment results demonstrate that MLB achieves better load balance, lower packet loss rate, and better routing connectivity ratio in both grid and random uniform topologies. MLB provides a more convincing routing solution for IoT applications.
Article
Full-text available
Traditional routing protocols are no longer suitable for the energy harvesting-wireless sensor networks (EH-WSN), which is powered by the energy harvested from environment instead of batteries. Rather than minimising the energy consumption and maximising the network lifetime, the main challenge in EH-WSN is to maximise its working performance under energy harvesting constraints. In this study, the authors propose a centralised power efficient routing algorithm energy harvesting genetic-based unequal clustering-optimal adaptive performance routing algorithm (EHGUC-OAPR) which contains two parts: (i) energy harvesting genetic-based unequal clustering algorithm EHGUC and (ii) optimal adaptive performance routing algorithm (OAPR). First, the base station (BS) uses EHGUC algorithm to form clusters of unequal size and select associated cluster heads, in which the clusters closer to the BS have smaller size. Then, the BS adopts OAPR algorithm to construct an optimal routing among each cluster heads. The numerical results show that EHGUC-OAPR is not only well applied to EH-WSN, but also has a great improvement in network energy balance and data delivery ratio.
Article
Full-text available
Due to scarce resources, such as transmission power, storage space and communication band-width, current broadcast approaches for general ad hoc networks can not be applied to IEEE 802.15.4 based ad hoc networks (e.g., ZigBee networks). This paper proposes a forward node selection algorithm that significantly reduces broadcast redundancy. The algorithm exploits the hierarchical address space in ZigBee networks. Only one-hop neighbor information is needed: a partial list of two-hop neighbors is derived at a node without exchanging messages between neighboring nodes. The complexity of the proposed algorithm is polynomial in terms of both computation time and memory space. The localized algorithm provides an optimal and feasible solution of selecting the minimum number of rebroadcast nodes in ZigBee networks, which is an NP-hard problem for general ad hoc networks. The proposed algorithm is extended to deal with packet loss during data transmission. A ZigBee rebroadcast algorithm is also proposed to further reduce the number of rebroadcast nodes and cover the whole network faster by assigning a non-random rebroadcast timer determined by the number of neighbors to be covered, distance and link quality. Simulations are conducted to evaluate the broadcast redundancy, coverage time, and coverage ratio.
Conference Paper
Full-text available
IEEE 802.15.4 standard is uniquely designed for low data rate wireless personal area networks (LR-WPANs). The IEEE 802.15.4 targets the applications such as industrial, agricultural, vehicular, residential, medical sensors and actuators which have more relaxed throughput requirements. ZigBee is a wireless technology based on IEEE 802.15.4. ZigBee routing uses ad hoc on-demand distance vector (AODV) routing protocol. In this paper we present an improved version of AODV called multipath energy aware AODV routing (ME-AODV), which utilizes the topology of network to divide it into one or more logical clusters and restricts the flooding of route request outside the cluster. The mesh links created at the time of cluster formation are used to decrease the routing path. ME-AODV uses nodes of the same cluster to share routing information, which significantly reduces the route path discovery. Since ZigBee routing is based on shortest-hop count, which causes overuse of a small set of nodes hence decreasing node as well as network lifetime. We also propose a mix of ad hoc on-demand multipath distance vector routing (AOMDV) and minimal-battery cost routing (MBCR) as an extension to AODV to increase the lifetime of network. The simulations have been performed using IEEE 802.15.4, ns-2 module.
Article
Two of the world's largest foundries—Taiwan Semiconductor Manufacturing Co. (TSMC) and Samsung—announced in April that they'd climbed one more rung on the Moore's Law ladder. TSMC spoke first, saying its 5-nanometer manufacturing process is now in what's called “risk production”—the company believes it has finished the process, but initial customers are taking a chance that it will work for their designs. Samsung followed quickly with a similar announcement.
Article
The insights contained in Gordon Moore's now famous 1965 and 1975 papers have broadly guided the development of semiconductor electronics for over 50 years. However, the field-effect transistor is approaching some physical limits to further miniaturization, and the associated rising costs and reduced return on investment appear to be slowing the pace of development. Far from signaling an end to progress, this gradual "end of Moore's law" will open a new era in information technology as the focus of research and development shifts from miniaturization of long-established technologies to the coordinated introduction of new devices, new integration technologies, and new architectures for computing.
Article
The automotive industry has been around for quite some time and it has evolved ever since, but the major transformation that is happening now from vehicles driven by humans to vehicles driven by themselves will have a long term impact on society. Today's cars are already connected and have been connected for some time, since they can link to smartphones, offer emergency roadside assistance, register real-time traffic alerts etc., but this evolution is about to change. The automobile industry is on the brink of a revolution, to move to self-driving automobile industry, and the driving force behind this is the fast developing technology, the Internet of Things (IoT). IoT will transform the automobile industry and at the same time, the automobile industry will provide a big boost to IoT. The potential and the prospects of this technology is astonishing. This paper examines the market and technical trends towards Autonomous Vehicles, evolution stages from early cars to fully autonomous, the importance of IoT in driving this industry ecosystem, advantages and disadvantages of Autonomous Vehincles, key issues and challenges faced by the industry, standards activities around this industry and finally the deployment use cases. The focus of this paper is more based on an industrial push to identify issues and challenges of Autonomous Vehicles and less on any academic research activity. The intention of this paper is to bring these issues and challenges to the attention of IFAC technical committee and trigger some debate on the opportunities for IFAC research in international stability.
Conference Paper
Remote health caring of patients at home is increasing with the popularity of various nature of mobile devices that has developed to enable remotely caring. The cloud as well as IoT (Internet of Things) and the mobile technologies make it easier to monitor the patients health conditions by sharing the health information to health care teams such as doctors, nurses and specialists. However the guardians of the patients can be anxious about their patients when they are in work. By ensuring guidance awareness about the patients, it can bring more liability of the hospital management. We have demonstrated a health care system for hospital management to allow guardians along with doctors to remotely monitor health conditions of patients via internet. Remote monitoring and guidance awareness by sharing information in a authenticated manner are the main focus.
Article
Wireless sensor networks (WSNs) are composed of a large number of inexpensive power-constrained wireless sensor nodes, which detect and monitor physical parameters around them through self-organization. Utilizing clustering algorithms to form a hierarchical network topology is a common method of implementing network management and data aggregation in WSNs. Assuming that the residual energy of nodes follows the random distribution, we propose a load-balanced clustering algorithm for WSNs on the basis of their distance and density distribution, making it essentially different from the previous clustering algorithms. Simulated tests indicate that the new algorithm can build more balanceable clustering structure and enhance the network life cycle.
Number of connected ioT devices will surge to 125 billion by 2030, IHS markit says
  • J Howell
Understanding autonomous vehicles
  • Asif Faisal
  • Md Kamruzzaman
  • T Y Currie