Erasure Codes for Reliable Communication in Internet of Things (IoT) Embedded with Wireless Sensors
01 Jan 2018-pp 115-137
TL;DR: The two methods of information transmission, end-to-end transmission and hop-by-hop transmission, prevalent in digital communication scenario is discussed in detail with emphasis on with and without erasure coding.
Abstract: With billions of devices adding up to the internet, broadly termed as Internet of Things (IoT), the need for reliable communication, distributed storage and computation has seamlessly increased. At this juncture, need for reliable communication, distributed storage and computation, adoption of error correcting codes and erasure codes plays a significant role. In this chapter, we give an overview of construction of erasure codes required for reliable communication with emphasis on Internet of Things (IoT) communication which have wireless sensors or sensor networks as its core. Wireless sensors form an integral part of Internet of Things (IoT) devices bridging the virtual world and the real world. Achieving reliability in such networks is highly desirable due to their broad range of applications. The discussed erasure codes in this chapter can be directly employed or with little modification in the context of reliable communication in Internet of Things. In this chapter, the two methods of information transmission, end-to-end transmission and hop-by-hop transmission, prevalent in digital communication scenario is discussed in detail with emphasis on with and without erasure coding. Also, the erasure codes used extensively in the context of achieving reliability in wireless sensor communication namely Reed-Solomon codes, Fountain codes and Decentralized Erasure codes are discussed and compared. This chapter serves as a starting point for researchers interested in working on reliability aspects of communication in Internet of Things embedded with wireless sensors and cyber physical systems.
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TL;DR: A new efficient-energy data dissemination scheme called data survivability with energy efficiency (DSwEE) that outperforms the existing schemes and achieves better performance than both DEC-DS and DEC-EaD in terms of the energy consumption and data recoverability for localized failures, which improves the lifespan of the network.
Abstract: Wireless sensor networks (WSNs) are mainly data-driven networks adopted to improve the Internet of Things (IoT) in terms of data throughput, energy efficiency, and self-management. Improving the data lifespan of WSN impacts the performance of the IoT. Achieving data reliability in applications of WSNs deployed in harsh environments is challenging due to the extreme constraints in resources of sensor nodes (SNs). Motivated by the inexpensive infrastructure of WSNs, a number of distributed storage systems have been proposed focusing on achieving data survivability rather than network reliability. In this article, we focus on data storage at the things layer (wireless sensors). We evaluate the performance of a number of distributed data storage systems (DDSSs) over WSN running over the ZigBee MAC protocol. Based on our findings, we introduce a new efficient-energy data dissemination scheme called data survivability with energy efficiency (DSwEE) that outperforms the existing schemes. We compare DSwEE against two prominent protocols in data storage, namely, decentralized erasure code for data survivability (DEC-DS) and decentralized erasure code encode-and-disseminate (DEC-EaD). Results show that DSwEE achieves better performance than both DEC-DS and DEC-EaD in terms of the energy consumption and data recoverability for localized failures, which improves the lifespan of the network.
10 citations
Additional excerpts
...They have been recently reviewed in [19]....
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TL;DR: Decentralized erasure codes were constructed from optimal locally recoverable codes and Blom’s key pre-distribution protocol, which exploits the generator matrix nature of constructed code, was implemented and it is shown that the proposed scheme performs better than other existing methods that use Reed–Solomon codes in terms of network load and error count per sent packet.
Abstract: Networks consisting of wireless sensors are extensively used in many applications. Decentralized Erasure Codes (DEC) were introduced to store the sensed data in the wireless sensor network itself before transmitting the sensed data from wireless nodes to gateway or query nodes. Data at the failed nodes can be reconstructed by accessing other nodes in the network in DEC setup. In a traditional DEC setup, it is required to access k nodes out of n nodes to reconstruct the data at the failed node. Also, the reconstruction at failed node is subjected to intruder attacks raising security concerns. To reduce the number of nodes required to reconstruct the data at failed nodes and to ensure communication security, in this paper, decentralized erasure codes were constructed from optimal locally recoverable codes and Blom’s key pre-distribution protocol, which exploits the generator matrix nature of constructed code, was implemented. From the simulations, we can infer that the proposed scheme performs better than other existing methods that use Reed–Solomon codes in terms of network load and error count per sent packet. Furthermore, it is shown that the proposed scheme ensures secure and efficient communication between nodes to overcome intruder interference.
3 citations
30 Dec 2019
TL;DR: The proposed method, where all data are read from raspberry pi, and sent to a pc server every minute using LAN and Wifi media, then update to a webserver was successfully implemented in the room condition telemonitoring.
Abstract: Data transmission system in IoT applications such as telemonitoring, using cable or wireless, requires a robust data transmission method. In this paper, a room condition telemonitoring application has been made where all data are read from raspberry pi, and sent to a pc server every minute using LAN and Wifi media, then update to a webserver. The data are sent using the method proposed in this paper which using the UDP mechanism with several improvements. Experiments have been done using LAN and Wifi, and it is found that the data can be received perfectly, although there are interferences from communication media. The average processing time on a raspberry pi is 19 ms, while the average processing time on the pc server is 5 us. In addition, the average processing speed of the transmission data from raspberry pi to a pc server until ack is received by raspberry pi using LAN media is 0.04 second, while using Wifi media is 0.08 seconds. Based on the experiments, it was found that the proposed method was successfully implemented in the room condition telemonitoring.
3 citations
Cites background from "Erasure Codes for Reliable Communic..."
...[6] have discussed a reliable communication in IoT Embedded with wireless sensors specifically regarding checking the originality of data and how to correct errors that occurring....
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TL;DR: This paper proposes a fast and reliable method for burst data transmission by fragmenting large data packets into blocks and introduces a burst transmission mechanism to optimize the EPC C1G2 communication procedure for burst transmission when there are critical and emergency data to be transmitted.
Abstract: Computational radio frequency identification (CRFID) sensors are able to transfer potentially large amounts of data to the reader in the radio frequency range. However, the existing EPC C1G2 protocol is inefficient when there are abundant critical and emergency data to be transmitted and cannot adapt to changing energy-harvesting and channel conditions. In this paper, we propose a fast and reliable method for burst data transmission by fragmenting large data packets into blocks and we introduce a burst transmission mechanism to optimize the EPC C1G2 communication procedure for burst transmission when there are critical and emergency data to be transmitted. In addition, we utilize erasure codes to reduce Acknowledgement (ACK) delay and to improve system reliability. Our results show that our proposed scheme significantly outperforms the current fixed frame length approach and the dynamic frame length and charging time adaptation scheme (DFCA) and that the goodput is close to the theoretically optimal value under different energy-harvesting and channel conditions.
2 citations
References
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TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
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