Communication Of Internet Of Things Devices Research Articles

Improving communication precision of IoT through behavior-based learning in smart city environment

Internet of Things (IoT) is an emergent heterogeneous communication platform that provides ubiquitous access to resources and meets the user demands. Developing smart city paradigm employs this communication platform for providing services to the users and granting distributed resource sharing. Mitigating adversary device selection in this platform prevents irrelevant data exchange and eases information access and exchange. External and internal communication hindering factors needs to be addressed to sustain uninterrupted communication. To improve the precision in communication security, an observation-based security system is modeled in this manuscript. This security system observes the local and global behavioral change in IoT device communication. The local and global behavioral changes are defined using device attributes and behavior modeling. The device and service provider input observations are processed by a neural network-based learning scheme to identify errors in the resource access. The communicating users and IoT devices are secured by selecting reputed service providers and data sources to improve the distributed resource utilization in IoT based smart city. The performance of the proposed scheme balances between security and resource utilization requirements of the users by reducing response loss and non-reputed device selection.

Using SMS for Communication with IoT Devices

This paper is in the context of Smart Cities and Internet of Things (IoT). We proposed a novel approach in communication of IoT devices via SMS messages. This can be beneficial during changing configuration of IoT devices or during sending data from IoT devices. SMS has some disadvantages in security and effective payload. As the security is an ongoing topic in this field, our solution manages to speedup key exchange process up to 62% compared to other solutions. We add the possibility of quick exchange of API key which is a key part in communication between IoT device and central server. With limitations of SMS length, we design the use of compression methods which can send 304 bytes in 140 bytes of a SMS body. Depending on the final usage of IoT device there is a possibility to use secure communication and/or compression. These new functions have only 4,3% time overhead in the process of sending data. That is negligible in devices which usually have to communicate in the case of predefined events or only a few times a day.

Glaze

Existing Internet of Things (IoT) solutions require expensive infrastructure for sending and receiving data. Emerging technologies such as ambient backscatter help fill this gap by enabling uplink communication for IoT devices. However, there is still no efficient solution to enable low-cost and low-power downlink communication for ambient backscatter systems. In this paper we present Glaze, a system that overlays data on existing wireless signals to create a new channel of downlink communication for IoT backscatter devices. In particular, Glaze uses a new technique that introduces small perturbations to existing signals to convey data. We evaluate the performance of Glaze and show how it can be used across wireless standards such as FM, TV, or Wi-Fi to communicate with devices with minimal impact on existing data transmissions.

Matched-Filter-Based Backscatter Communication for IoT Devices Over Ambient OFDM Carrier

In this article, we study backscatter communication (BC) for power-limited devices that are connected to a network for the Internet of Things (IoT), where joint estimation and detection is carried out at a receiver to detect signals from a backscatter device (BD) with ambient orthogonal frequency-division multiplexing (OFDM) carrier. In conventional BC, in order to avoid the difficulty of the carrier estimation, the energy detector is usually considered at a receiver at the cost of poor performance. To improve the performance, in this article, we consider a novel approach that allows the carrier estimation at the receiver via joint estimation and detection. In particular, in the proposed approach, the matched-filtering at the BD (for the transmitter filter) is employed to impose a certain property that allows efficient and reliable carrier estimation via joint estimation and detection. Through the performance analysis and simulation results, we show that the matched-filtering at the BD in the proposed approach can improve the performance.

Genetic algorithm based adaptive offloading for improving IoT device communication efficiency

Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communicating devices and user request, traffic handling and delay-less service are unenviable. This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. The process of offloading is designed to mitigate unnecessary delays in request process and to improve the success rate of the IoT requests. The fitness process of GA is distributed among the gateways and infrastructure to handle requests satisfying different communication metrics. The process of GA balances between the optimal and sub-optimal solutions generated to improve the rate of request response. Experimental results prove the consistency of the proposed GA-OA by improving request success ratio, achieving lesser complexity, delay and processing time.

Modelling IoT devices communication employing representative operation modes to reveal traffic generation characteristics

Several traffic models for the Internet of Things (IoT) have been proposed in the literature. However, they can be considered as heuristic models since they only reflect the stochastic characteristic of the generated traffic. In this paper, we propose a model to represent the communication of IoT devices. The model was used to obtain the traffic generated by the devices. Therefore, the proposed model is able to capture a wider understanding of device behaviour than existing, state-of-the-art traffic models. The proposed model illustrates the behaviour of Machine-to-Machine uplink communication in a network with multiple-access limited information capacity shared channels. In this paper, we analysed the number of transmitted packets using the traffic model extracted from our proposed communication model and compared it with the state-of-the-art traffic models using simulations. The simulation results show that the proposed model has significantly higher accuracy in estimating the number of transmitted packets compared with the current models in the literature.

Turning the Signal Interference Into Benefits: Towards Indoor Self-Powered Visible Light Communication for IoT Devices in Industrial Radio-Hostile Environments

Internet of Things (IoT) plays as the key infrastructure for various applications, e.g., smart city, smart grid, and smart manufacturing. Wireless communication is greatly limited for IoT devices due to the severe electromagnetic interference in industrial radio-hostile environments. Visible light communication (VLC) has been considered as a promising communication technique that could complement the wireless communication. Due to complex industrial environments, it is not practical to wire such scale of VLC-enabled IoT devices into the power line or replace batteries for them. To cope with this challenge, we propose to harvest the energy from electromagnetic interference signals to power the VLC for IoT devices. We achieve this goal by proposing advanced signal processing designs, new energy-efficient schemes, and seamless integration. Finally, we prototype the system on real devices, and our extensive experiments show that our system can achieve promising results.

A Study on the Design of Fog Computing Architecture Using Sensor Networks.

It is expected that the number of devices connecting to the Internet-of-Things (IoT) will increase geometrically in the future, with improvement of their functions. Such devices may create a huge amount of data to be processed in a limited time. Under the IoT environment, data management should play the role of an intermediate level between objects and devices that generate data and applications that access to the data for analysis and the provision of services. IoT interactively connects all communication devices and allows global access to the data generated by a device. Fog computing manages data and computation at the edge of the network near an end user and provides new types of applications and services, with low latency, high frequency bandwidth and geographical distribution. In this paper, we propose a fog computing architecture for efficiently and reliably delivering IoT data to the corresponding IoT applications while ensuring time sensitivity. Based on fog computing, the proposed architecture provides efficient power management in IoT device communication between sensors and secure management of data to be decrypted based on user attributes. The functional effectiveness and the safe data management of the method proposed are compared through experiments.