The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications ...whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.
Federated learning (FL) is a distributed machine learning strategy that generates a global model by learning from multiple decentralized edge clients. FL enables on-device training, keeping the ...client's local data private, and further, updating the global model based on the local model updates. While FL methods offer several advantages, including scalability and data privacy, they assume there are available computational resources at each edge-device/client. However, the Internet-of-Things (IoT)-enabled devices, e.g., robots, drone swarms, and low-cost computing devices (e.g., Raspberry Pi), may have limited processing ability, low bandwidth and power, or limited storage capacity. In this survey article, we propose to answer this question: how to train distributed machine learning models for resource-constrained IoT devices? To this end, we first explore the existing studies on FL, relative assumptions for distributed implementation using IoT devices, and explore their drawbacks. We then discuss the implementation challenges and issues when applying FL to an IoT environment. We highlight an overview of FL and provide a comprehensive survey of the problem statements and emerging challenges, particularly during applying FL within heterogeneous IoT environments. Finally, we point out the future research directions for scientists and researchers who are interested in working at the intersection of FL and resource-constrained IoT environments.
Applying blockchain technology to the Internet of Things (IoT) provides several advantages when compared to conventional systems, including improving the security by ensuring data integrity and ...accountability while enabling reliable control over many devices. However, integrating blockchain into IoT systems presents some challenges. A main challenges is designing a consensus protocol that is suitable for the IoT systems, where some devices may lack adequate resources, such as computation power. This paper introduces a novel consensus protocol called honesty-based distributed proof of authority (HDPoA) via scalable work. HDPoA is based on proof of authority (PoA) and proof of work (PoW), with the integration of PoW, HDPoA is able to realize the security advantages provided by PoW. This is achieved by utilizing the IoT devices’ collective computation power to mine and generate a new block.
HDPoA was analyzed and then deployed and tested utilizing a purposely built testbed incorporating commercial devices that are low-cost. A performance measurements and evaluation along with the security analyses of HDPoA was conducted using a total of 30 different IoT devices comprise of Raspberry Pis, ESP32, and ESP8266. These measurements included energy consumption, devices’ battery life, devices’ hash power, and the mining time. The measured values of hash per joule (h/J) for mining were 13.8 Kh/J, 54 Kh/J, and 22.4 Kh/J when using the Raspberry Pis, the ESP32 devices, and the ESP8266 devices, respectively. The measured devices’ hash power, measured energy consumption, and the security analyses showed that HDPoA is secure and suitable for utilization into IoT-blockchain systems.
In this paper, a multimode smart nonlinear circuit (MSNC) for wireless communications (Tx and Rx modes) as well as energy harvesting (EH) and power saving is presented. The proposed MSNC is designed ...at 680 MHz and has three ports, which are connected to an antenna, and T/R (transceiver) and power-saving modules. According to the input/output power level, the proposed MSNC has three modes of operations; Receiving (Rx), power saving and transmitting (Tx), for low (<-25 dBm), mid (>-25 dBm and <0 dBm) and high (>5 dBm) power ranges, respectively. In the power-saving mode, when the received power is greater than the sensitivity of the Rx module, the excess power is directed to the energy harvesting load (power storage), while the receiving direction is still in place. The fact that the proposed MSNC can manage the received power level smartly and without any external control, distinguishes the proposed MSNC from other EH circuits. The proposed MSNC operates within a power range from -50 dBm to +50 dBm, demonstrates an efficiency of more than 60% in the power-saving mode, and has acceptable matching over a large frequency range. The design procedure of the proposed MSNC along with the theoretical, simulation and measurement results are presented in this paper. Good agreement between theory, simulation and measurement results confirms the accuracy of design procedure.
In this letter, a compact beamsteering MIMO antenna is proposed for Directional Modulation (DM) applications. A generalization of the beamsteering principle is presented and by exploiting the ...antenna's unidirectional beamsteering capability within the entire azimuth plane, the system realizes secure steerable transmissions in the direction of the legitimate receiver, with a low Bit Error Rate (BER) of <inline-formula><tex-math notation="LaTeX">\boldsymbol{10^{-5}}</tex-math></inline-formula>, while a high error rate of <inline-formula><tex-math notation="LaTeX">\boldsymbol{10^{-1}}</tex-math></inline-formula> is seen outside the desired regions. Numerical and experimental verifications are carried out to validate the proposed concept and comparisons with circular arrays are conducted, revealing that the system achieves comparable performance but with up to <inline-formula><tex-math notation="LaTeX">\boldsymbol{35\%}</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">\boldsymbol{71\%}</tex-math></inline-formula> reduction in antenna diameter and profile, respectively. The method is proposed as a good candidate to enhance the security of small (IoT) devices.
The Internet of Things (IoT) is a global ecosystem of information and communication technologies aimed at connecting any type of object (thing), at any time, and in any place, to each other and to ...the Internet. One of the major problems associated with the IoT is the heterogeneous nature of such deployments; this heterogeneity poses many challenges, particularly, in the areas of security and privacy. Specifically, security testing and analysis of IoT devices is considered a very complex task, as different security testing methodologies, including software and hardware security testing approaches, are needed. In this paper, we propose an innovative security testbed framework targeted at IoT devices. The security testbed is aimed at testing all types of IoT devices, with different software/hardware configurations, by performing standard and advanced security testing. Advanced analysis processes based on machine learning algorithms are employed in the testbed in order to monitor the overall operation of the IoT device under test. The architectural design of the proposed security testbed along with a detailed description of the testbed implementation is discussed. The testbed operation is demonstrated on different IoT devices using several specific IoT testing scenarios. The results obtained demonstrate that the testbed is effective at detecting vulnerabilities and compromised IoT devices.
With the increasing advancement in the applications of the Internet of Things (IoT), the integrated Cloud Computing (CC) faces numerous threats such as performance, security, latency, and network ...breakdown. With the discovery of Fog Computing these issues are addressed by taking CC nearer to the Internet of Things (IoT). The key functionality of the fog is to provide the data generated by the IoT devices near the edge. Processing of the data and data storage is done locally at the fog node rather than moving the information to the cloud server. In comparison with the cloud, Fog Computing delivers services with high quality and quick response time. Hence, Fog Computing might be the optimal option to allow the Internet of Things to deliver an efficient and highly secured service to numerous IoT clients. It allows the administration of the services and resource provisioning outside CC, nearer to devices, at the network edge, or ultimately at places specified by Service Level Agreements (SLA’s). Fog Computing is not a replacement to CC, but a prevailing component. It allows the processing of the information at the edge though still delivering the option to connect with the data center of the cloud. In this paper, we put forward various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with IoT, various fog system algorithms and also systematically examine the challenges in Fog Computing which acts as a middle layer between IoT sensors or devices and data centers of the cloud.
Communicating Things Network (CTN) is the latest paradigm in the development of smart technologies. CTN comprises a network of physical devices capable of extracting and sharing digital information. ...The aim of CTN is to develop smart appliances that boost productivity and provide real-time data rapidly than any structure or a network that is dependent on human interference. Interconnected physical objects in the network communicate with each other and facilitate intelligent decision-making by monitoring and analysing their surroundings. In today's era, CTNs are playing a significant role in daily activities by providing a substantial reduction in costs with increased visibility and efficiency in all aspects of businesses and individuals. In this manuscript, we have considered an important application of CTNs (i.e., IoT) and have proposed a secure Hybrid Industrial IoT framework using the Blockchain technique. We have used a hybrid industrial architecture where different branches of a company are located in more than one country. Although IoT devices are used in many organizations and assist in reducing their production costs along with improving quality, several threats can occur in IoT devices initiated by various intruders. Intruders may compromise IoT devices with the purpose of performing malicious activities. For example, a company's employee may steal some product or may rest during working hours. To prevent these issues, the Blockchain technology is considered as the best technique that provides secrecy and protects the control system in real-time conditions. In this manuscript, we have used a Blockchain mechanism to extract information from IoT devices and store extracted records into Blockchain to maintain transparency among various users located at different places. Furthermore, the experimentation of the proposed framework has been performed against the internal communication of Blockchain where IoT devices are compromised by several intruders. Results have been analysed against the conventional approach and validated with improved simulated results that offer an 89% success rate over user request time, falsification attack, black hole attack, and probabilistic authentication scenarios because of the Blockchain technology.
With a massive number of Internet-of-Things (IoT) devices connecting with the Internet via 5G or beyond 5G (B5G) wireless networks, how to support massive access for coexisting cellular users and IoT ...devices with quality-of-service (QoS) guarantees over limited radio spectrum is one of the main challenges. In this paper, we investigate the multi-operator dynamic spectrum sharing problem to support the coexistence of rate guaranteed cellular users and massive IoT devices. For the spectrum sharing among mobile network operators (MNOs), we introduce a wireless spectrum provider (WSP) to make spectrum trading with MNOs through the Stackelberg pricing game. This framework is inspired by the active radio access network (RAN) sharing architecture of 3GPP, which is regarded as a promising solution for MNOs to improve the resource utilization and reduce deployment and operation cost. For the coexistence of cellular users and IoT devices under each MNO, we propose the coexisting access rules to ensure their QoS and the priority of cellular users. In particular, we prove the uniqueness of the Stackelberg equilibrium (SE) solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative algorithm for the Stackelberg pricing game, which is proved to achieve the unique SE solution. Extensive numerical simulations demonstrate that, the payoffs of WSP and MNOs are maximized and the SE solution can be reached. Meanwhile, the proposed multi-operator dynamic spectrum sharing algorithm can support more than almost 40% IoT devices compared with the existing no-sharing method, and the gap is less than about 10% compared with the exhaustive method.
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