scholarly journals A Proactive role of IoT devices in building Smart Cities

Author(s):  
Shahzad Ashraf
Author(s):  
Rajan R. ◽  
Venkata Subramanian Dayanandan ◽  
Shankar P. ◽  
Ranganath Tngk

A smart city aims at developing an ecosystem wherein the citizens will have instant access to amenities required for a healthy and safe living. Since the mission of smart city is to develop and integrate many facilities, it is envisaged that there is a need for making the information available instantly for right use of such infrastructure. So, there exists a need to design and implement a world-class physical security measures which acts as a bellwether to protect people life from physical security threats. It is a myth that if placing adequate number of cameras alone would enhance physical security controls in smart cities. There is a need for designing and building comprehensive physical security controls, based on the principles of “layered defense-in-depth,” which integrates all aspects of physical security controls. This chapter will review presence of existing physical security technology controls for smart cities in line with the known security threats and propose the need for an AI-enabled physical security premise.


Internet of Things (IoT) is efficiently plays vital role in development of several sectors by offering many opportunities to grow the economy and improve the life standard through connecting billions of “Things” which provides business opportunities in different sectors and encounter many technical and application challenges. This paper emphasizes the role of Dynamic bandwidth allocation and protocols standards in various IoT sectors such as healthcare, education, agriculture, industrial, transportation, smart cities etc., and focuses on the challenges in providing uninterrupted bandwidth to all IoT devices with existing infrastructure, which depends on standardized protocols and network devices to establish connection with heterogeneous IoT devices. This paper covers Enhanced Dynamic Bandwidth Techniques, protocol standards and policies in IoT network technologies to Improve QoS in IoT devices.


2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.


2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.


2021 ◽  
Vol 13 (9) ◽  
pp. 4716
Author(s):  
Moustafa M. Nasralla

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.


Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 552 ◽  
Author(s):  
Rocksan Choi ◽  
SeungGwan Lee ◽  
Sungwon Lee

In our modern world, many Internet of Things (IoT) technologies are being researched and developed. IoT devices are currently being used in many fields. IoT devices use Wi-Fi and Bluetooth, however, communication distance is short and battery consumption is high. In areas such as smart cities and smart farms, IoT technology is needed to support a wide coverage with low power consumption. Low Power Wide Area (LPWA), which is a transmission used in IoT supporting a wide area with low power consumption, has evolved. LPWA includes Long Range (LoRa), Narrowband (NB-IoT), and Sigfox. LoRa offers many benefits as it communicates the longest distances, is cheap and consumes less battery. LoRa is used in many countries and covers a range of hundreds of square kilometers (km2) with a single gateway. However, if there are many obstacles to smart cities and smart farms, it causes communication problems. This paper proposes two (2) solutions to this problem: the relay method which is a multi-hop method and the Automatic Repeat Request (ARQ) system that detects packet loss in real-time and requests retransmission for LoRa. In this study, the actual performance of LoRa in the problematic environment was measured and the proposed method was applied. It was confirmed that the transmission rate of LoRa dropped when there were many obstacles such as trees. To use LoRa in a smart farm with a lot of space, multi-hop was observed to be better. An ARQ system is needed to compensate for the unexpected drop in the forward rate due to the increase in IoT devices. This research focused on reliability, however, additional network methods and automatic repeat request (ARQ) systems considering battery time should be researched in symmetry. This study covers the interdisciplinary field of computer science and wireless low power communication engineering. We have analyzed the LoRa/LoRaWAN technology in an experimental approach, which has been somewhat less studied than cellular network or WiFi technology. In addition, we presented and improved the performance evaluation results in consideration of various local and climatic environments.


Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1598
Author(s):  
Sigurd Frej Joel Jørgensen Ankergård ◽  
Edlira Dushku ◽  
Nicola Dragoni

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.


Author(s):  
Domenico Garlisi ◽  
Alessio Martino ◽  
Jad Zouwayhed ◽  
Reza Pourrahim ◽  
Francesca Cuomo

AbstractThe interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach.


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