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The race to the internet of things
... These devices communicating with each other can create a heterogeneous network, providing a large number of services called "Internet of Things" or IoT. 1 "Internet of Things" as a term and definition was coined by Kevin Ashton of MIT in 1999. 2 Looking at the trend and rapid pace at which number of devices is growing, more than 30 billion internet connected devices will exist by 2025. 3 IoT has several applications and is already being used in many sectors including health, logistics, smart grids, autonomous vehicles, and many more. 4 One particular scenario of IoT where most of the connected devices in the network are vehicles is called Internet of Vehicles (IoV). ...
Internet of Vehicles (IoV) is an internet enabled connected network of vehicles that provide services more effectively to its users via data sharing and communication with other vehicles and objects in the network. Use of fog servers to create a vehicular cloud which is connected to a primary cloud server is another effective technique that gives better performance by achieving better cooperation between the vehicular cloud and the internet. However, due to dependence on wireless technologies for communication, there is a need for a secure IoV network that can authenticate each device connecting to the network and can further secure the communication among these devices. This article introduced a secure authentication and session key management scheme for IoV (SASM‐IoV) working in fog computing‐based IoV network. SASM‐IoV first authenticates objects before entering into the network and then creates session keys for further communication to be secure. Devices registered to the network are assigned temporary identities, further used in the authentication process, thereby making our scheme more secure. SASM‐IoV protocol is effective against many security attacks such as impersonation, man‐in‐the‐middle, replay attack, and many more. Informal security analysis performed over SASM‐IoV shows that it is resilient against most security attacks. We have also performed a formal security analysis of the scheme using the widely accepted AVISPA tool. Network simulation and communication cost comparisons show that SASM‐IoV is better in terms of different network parameters and provides better security with lower costs when compared to similar security schemes.
... Among these technologies is IoT, which enables us to harvest raw data with the help of sensors and micro-controllers [21]. IoT has revolutionized the way we live from healthcare to traffic control to safeguarding homelands and even the energy grid systems [26,51]. One such revolutionized The researchers provided a framework named IoTNetWar, which connected sensors, gateways, military personnel, and weapons, and so on This paper just provided an unevaluated cloud-based framework for the proposed system. ...
The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices, wearables, sensors, and micro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-To-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions.
... This problem wouldn't be serious if these systems were configured properly, but most of them are vulnerable to account harvesting which is resulted by the possibility of user enumeration, weak password policy and lack of account lockout. [13] Harvesting means collecting account names or related information and potentially scam people through exploiting the collected information, e.g., spamming email advertising. User enumeration means the ability to determine accounts names and related information. ...
Aujourd’hui, les nombreuses applications de l’Internet des Objets (IoT : Internet of Things) peuvent significativement améliorer la vie quotidienne des utilisateurs. Grâce à ces applications, il est possible de commander à distance les différents appareils de la maison, surveiller les signes vitaux d’un patient et alerter automatiquement son médecin en cas de problème. Cependant, les problèmes de sécurité et de protection de la vie privée empêchent les utilisateurs de faire pleinement confiance à ces applications, ce qui peut avoir pour effet de ralentir l’adoption globale de ces technologies et leur large déploiement. Pour résoudre ces problèmes de sécurité et de protection de la vie privée, plusieurs solutions ont été proposées. Cependant, plusieurs défis restent encore à relever pour permettre une large adoption de ces applications. L’approche centrée sur l’utilisateur semble être très pertinente pour relever un grand nombre de ces défis. Pour offrir une sécurité et une protection de la vie privée centrées sur l’utilisateur et permettre la prise en charge de nombreuses applications IoT, les travaux de cette thèse proposent d’adapter la mise en œuvre des mécanismes de sécurité et de protection de la vie privée en fonction du contexte de l’utilisateur. Dans un premier temps, cette thèse présente l’architecture CASPaaS (Context-Aware Security and Privacy as a Service). Cette architecture de sécurité et de protection de la vie privée sensibles au contexte pour l’IoT est basée sur l’approche ‘as a service’. Elle garantit l’adaptation dynamique et personnalisée des services de sécurité et de protection de la vie privée en fonction du contexte de l’utilisateur. Grâce à la conception ‘as a service’, cette architecture se caractérise par une grande flexibilité qui lui permet de prendre en charge de nombreuses applications IoT. Dans un second temps, cette thèse présente un système permettant de gérer la sécurité et la fiabilité de l’architecture CASPaaS elle-même. Ce système, appelé SETUCOM (SEcure and TrUstworthy COntext Management) gère la sécurité des données contextuelles échangées au sein de l’architecture CASPaaS ainsi que la confiance des sources de données. Ceci permet de pallier un grand nombre d’attaques pouvant conduire au disfonctionnement de notre architecture CASPaaS. Dans un troisième temps, cette thèse présente un nouveau système de gestion décentralisée des autorisations sensibles au contexte pour l’IoT. Ce système, basé également sur l’approche ‘as a service’, offre à l’utilisateur une gestion dynamique, décentralisée et simple des autorisations. Dans un dernier temps, cette thèse s’intéresse au déploiement du service CASPaaS au plus proche des utilisateurs en se basant sur une infrastructure de type Edge Computing. Dans ce contexte, nous proposons une nouvelle stratégie de placement dynamique de ce service. Cette stratégie fait appel à des techniques de l’intelligence artificielle afin de garantir un placement efficace tout en optimisant les différentes performances (réseau, service, capacités des nœuds Edge, processus de placement lui-même, etc.).
Wireless Sensor Networks (WSN) play a critical role in Internet of Things (IoT). It is essentially the interface between the IoT and the physical world. This chapter describes the characteristics of the WSN and analyzes the power constraints, the network operation, the operating system, and the computing paradigm resulted from these characteristics. IoT needs High-Performance Computing (HPC) to resolve its optimization problems. How to efficiently use the embedded, distributed, or hosted computing power in IoT is the fundamental principle of addressing the challenges in constructing a smart world. New approaches to HPC are made possible by advances in microprocessor architectures which include proliferation of parallelism and high-bandwidth networks. Several solutions exist, such as Graphics Processing Unit (GPU) computing, Many Integrated Core (MIC) computing, heterogeneous computing, Peer-to-Peer (P2P) computing, Cloud computing, and Grid computing. The chapter discusses two operating systems that are emerged in the research of sensor networks, including TinyOS and LiteOS.
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