REED SOLOMON CODES FOR RELIABLE COMMUNICATION IN INTERNET OF THINGS (IOT)

In the networking field, Internet of Things ( IOT, shortly) is the current state of the art in the nowadays Information of Technology era. The networking may be defined as external network or internal network. The backbone of the IOT is the internet connections. The IOT connects various objects together to the internet so that they can communicate and exchange billions of data and information among various devices and services. They may be remotely controlled from distant area. As IOT systems will be open and available everywhere, a number of security issue may arise. One issue that remains open in the IOT technology is security and privacy issues. Because of this security issue, the communications among many different devices powered by IOT could not be said as a reliable technology. Because of this, the security of the IOT systems can be enhanced by adding error correction scheme both in communication channel as well as the data store. By introducing the error correction scheme, the risks may be reduced to acceptable level and the security could be enhanced. A Reed-Solomon (RS) code is one of many error control coding schemes that firstly introduced by Reed and Solomon in 1960. This code has been used in various applications, such as CD-ROMs, space communications, DVD technology, digital TV and much more. Here, Reed-Solomon code is discussed in detail. It raised the issue of RS decoding scheme using the Welch Berlekamp algorithm. It presents the implementation of the Welch Berlekamp algorithm for RS decoder in detail. The VHDL implementation VHDL for Hard Decision Decoding using the Welch Berlekamp algorithm is also presented. Without loss of generality, th

A Hybrid Adaptive Protocol for Reliable Data Delivery in WSNs with Multiple Mobile Sinks

In this paper we deal with reliable and energy-efficient data delivery in sparse Wireless Sensor Networks with multiple Mobile Sinks (MSs). This is a critical task, especially when MSs move randomly, as interactions with sensor nodes are unpredictable, typically of short duration, and affected by message losses. In this paper we propose an adaptive data delivery protocol that combines efficiently erasure coding with an ARQ scheme. The key features of the proposed protocol are: (i) the use of redundancy to cope efficiently with message losses, and (ii) the ability of adapting the level of redundancy based on feedbacks sent back by MSs through ACKs. We observed by simulation that our protocol outperforms an alternative protocol that relies only on an ARQ scheme, even when there is a single MS. We also validated our simulation results through a set of experimental measurements based on real sensor nodes. Our results show that the adoption of encoding techniques is beneficial to energy-efficient (and reliable) data delivery in WSNs with Mobile Sinks

Comparative study of a time diversity scheme applied to G3 systems for narrowband power-line communications

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Masters of Science in Engineering (Electrical). Johannesburg, 2016Power-line communications can be used for the transfer of data across electrical net- works in applications such as automatic meter reading in smart grid technology. As the power-line channel is harsh and plagued with non-Gaussian noise, robust forward error correction schemes are required. This research is a comparative study where a Luby transform code is concatenated with power-line communication systems provided by an up-to-date standard published by electricit e R eseau Distribution France named G3 PLC. Both decoding using Gaussian elimination and belief propagation are imple- mented to investigate and characterise their behaviour through computer simulations in MATLAB. Results show that a bit error rate performance improvement is achiev- able under non worst-case channel conditions using a Gaussian elimination decoder. An adaptive system is thus recommended which decodes using Gaussian elimination and which has the appropriate data rate. The added complexity can be well tolerated especially on the receiver side in automatic meter reading systems due to the network structure being built around a centralised agent which possesses more resources.MT201

Artificial Intelligence Aided Receiver Design for Wireless Communication Systems

Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as powerful assistance to implement two receiver designs in two different use-cases. We considered a DNN-based joint baseband demodulator and channel decoder (DeModCoder), and a DNN-based joint equalizer, baseband demodulator, and channel decoder (DeTecModCoder) in two single operational blocks, respectively. The multi-label classification (MLC) scheme was equipped to the output of conducted DNN model and hence yielded lower computational complexity than the multiple output classification (MOC) manner. The functional DNN model can be trained offline over a wide range of SNR values under different types of noises, channel fading, etc., and deployed in the real-time application; therefore, the demands of estimation of noise variance and statistical information of underlying noise can be avoided. The simulation performances indicated that compared to the corresponding conventional receiver signal processing schemes, the proposed AI-aided receiver designs have achieved the same bit error rate (BER) with around 3 dB lower SNR

A Lite Distributed Semantic Communication System for Internet of Things

The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs the DL based semantic communication (DeepSC) model training and updating while IoT devices perform data collection and transmission based on the trained model. To make it affordable for IoT devices, we propose a lite distributed semantic communication system based on DL, named L-DeepSC, for text transmission with low complexity, where the data transmission from the IoT devices to the cloud/edge works at the semantic level to improve transmission efficiency. Particularly, by pruning the model redundancy and lowering the weight resolution, the L-DeepSC becomes affordable for IoT devices and the bandwidth required for model weight transmission between IoT devices and the cloud/edge is reduced significantly. Through analyzing the effects of fading channels in forward-propagation and back-propagation during the training of L-DeepSC, we develop a channel state information (CSI) aided training processing to decrease the effects of fading channels on transmission. Meanwhile, we tailor the semantic constellation to make it implementable on capacity-limited IoT devices. Simulation demonstrates that the proposed L-DeepSC achieves competitive performance compared with traditional methods, especially in the low signal-to-noise (SNR) region. In particular, while it can reach as large as 40x compression ratio without performance degradation.Comment: Accpeted by JSA

Satellite Networks: Architectures, Applications, and Technologies

Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled