BACKGROUND

Technical Field

The present disclosure is directed to a system, method and apparatus for compressive sensing-aided LPWAN MAC layer for LoRa communication devices for communication with a sparse recovery receiver.

Description of Related Art

The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.

Research in low power wide area networks (LPWAN) in the field of Internet of Things (IoT) communications is intended to address the short range and high-power consumption of conventional IoT technologies. LPWAN technologies have exposed the need for reliable knowledge about the performance of such networks in the IoT market. Projections have been made that more than seven billion of a projected thirty billion IoT devices will be LPWAN-based, as shown inFIG. 1. (See E. Berthelsen and J. Morrish, “Forecasting the internet of things revenue opportunity.” ttps://www.machinaresearch.com, 2015. [Online; accessed 30 Nov. 2019]; and Nokia White Paper, “LTE evolution for IoT connectivity.” world wide web.resources.alcatel-ucent.com/asset/200178, 2016. [Online; accessed 30-Nov.-2017], each incorporated herein by reference in their entirety).

LoRa, (Long Range) is an LPWAN protocol based on spread spectrum modulation techniques derived from chirp spread spectrum (CSS) technology. A LoRaWAN-based network is made up of end devices, gateways, a network server, and application servers. End devices send data to gateways (uplinks), and the gateways pass it on to the network server, which, in turn, passes it on to the application server as necessary. End devices in a LoRaWAN network come in three classes: Class A, Class B and Class C. While end devices can always send uplinks at will, the device's class determines when it can receive downlinks. The class also determines a device's energy efficiency. The more energy efficient a device, the longer the battery life.

All end devices must support Class A (“Aloha”) communications. Class A end devices spend most of their time in sleep mode. Because LoRaWAN is not a “slotted” protocol, end devices can communicate with the network server any time there is a change in a sensor reading or when a timer fires. Basically, they can wake up and talk to the server at any moment. After the device sends an uplink, it “listens” for a message from the network one and waits two seconds after the uplink (receive windows) before going back to sleep. Class A is the most energy efficient and results in the longest battery life.

While LoRa is capable of transmission ranges up to 20 km, the achievable rate, as well as well as the data exchange timeframe, are very limited. Additionally, with an increase in the number of sources, and the use of a LoRa-Class A pure Aloha Multiple Access (MAC) scheme, network congestion is inevitable. Therefore, it is desirable to reduce the number of times each source contends for multiple access without loss of data.

An improvement to the original ALOHA protocol was “Slotted ALOHA”, which introduced discrete timeslots and increased the maximum throughput. A station can start a transmission only at the beginning of a timeslot, and thus collisions are reduced. In this case, only transmission-attempts within 1 frame-time and not 2 consecutive frame-times need to be considered, since collisions can only occur during each timeslot. Thus, the probability of there being zero transmission-attempts by other stations in a single timeslot is Probslotted=e−Gthe probability of a transmission requiring exactly k attempts is (k−1 collisions and 1 success): Probslottedk=e−G(1−e−G)k-1and the throughput is: Sslotted=Ge−G, where G denotes transmission attempts per frame time. The maximum throughput is 1/e frames per frame-time (reached when G=1), which is approximately 0.368 frames per frame-time, or 36.8%.

Slotted ALOHA is used in low-data-rate tactical satellite communication networks by military forces, in subscriber-based satellite communications networks, mobile telephony call setup, set-top box communications and in the contactless RFID technologies.

The Low Power Wide Area Network (LPWAN) has emerged as a new IoT communication paradigm. (See U. Raza, P. Kulkarni, and M. Sooriyabandara, “Low power wide area networks: An overview,” IEEE Communications Surveys & Tutorials Vol. 19, No. 2, pp. 855-873, 2017, incorporated herein by reference in its entirety). LPWAN has been proposed to tackle the issues of short range and high-power consumption of the conventional IoT technologies. LPWAN aims to complement the current IoT market by offering a unique aspect of wide-area connectivity while keeping end-device battery consumption minimal. In contrast to conventional short-range non-cellular IoT technologies, such as Zig-Bee, Bluetooth, and Wi-Fi, a single LPWAN gateway can cover up to 20 km in range.

Although cellular networks can provide wide-range coverage, their end-devices cannot achieve the five to ten years battery lifespan that LPWAN counterparts can offer. (See Raza et al.; and H. Wang and A. O. Fapojuwo, “A survey of enabling technologies of low power and long range machine-to-machine communications,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2621-2639, 2017, each incorporated herein by reference in their entirety).

However, wide-range connectivity and low-power consumption performance come at the expense of low data rate, low reliability, and high latency. (See Raza et al.; and W. Yang, M. Wang, J. Zhang, J. Zou, M. Hua, T. Xia, and X. You, “Narrowband wireless access for low-power massive internet of things: A bandwidth perspective,” IEEE Wireless Communications, vol. 24, no. 3, pp. 138-145, 2017, each incorporated herein by reference in their entirety). From this perspective, IoT networks can be categorized into two machine-type communication (MTC) schemes, namely, massive non-critical MTC and critical MTC. LPWAN fulfills the massive non-critical MTC communication in which the data rate, reliability, and latency are relaxed. The critical-based MTC, such as vehicle-to-vehicle (V2V) communication and traffic safety applications, are out of the scope of current LPWAN technologies since extreme low-latency, and high-reliability are stringent requirements. (See E. Berthelsen and J. Morrish, “Forecasting the internet of things revenue opportunity.” world wide web.machinaresearch.com, 2015. [Online; accessed 30 Apr. 2019]; and Nokia White Paper, “LTE evolution for IoT connectivity.” world wide web.resources.alcatel-ucent.com/asset/200178, 2016. [Online; accessed 30 Apr. 2019], each incorporated herein by reference in their entirety).

Research in LPWAN technologies can be divided into two main categories. The first category is technologies that are based on unlicensed spectrums, such as SIGFOX, LoRa and Ingenu. (See J. C. Zuniga and B. Ponsard, “Sigfox system description,” LPWAN@ IETF97, November 14th, 2016; LoRa-Alliance, “A technical overview of LoRaWAN,” Technical Marketing Workgroup, November 2015; and Ingenu, “How RPMA works?” White Paper, 2016, each incorporated herein by reference in their entirety). The second category includes technologies that are using the licensed spectrum and utilizing the cellular infrastructure, namely, Narrow-Band IoT (NB-IoT), LTE-M and EC-GSM. (See Wang et al.; P. Thubert, A. Pelov, and S. Krishnan, “Low-power wide-area networks at the ietf,” IEEE Communications Standards Magazine, vol. 1, pp. 76-79, March 2017; D. Flore, “3GPP standards for the Internet-of-Things,” Recuperado el, vol. 25, 2016; and J. Lee, Y. Kim, Y. Kwak, J. Zhang, A. Papasakellariou, T. Novlan, C. Sun, and Y. Li, “LTE-advanced in 3GPP rel-13/14: An evolution toward 5G,” IEEE Communications Magazine, vol. 54, no. 3, pp. 36-42, each incorporated herein by reference in their entirety). Some conventional LPWAN technologies are compared in Table I.

TABLE 1Comparison of LPWAN technologies.SIGFOX [6]LoRa [7]INGENU [8]NB-IoT [2, 9]Range (URBAN)>5 km>15 km>10 km>20 kmOperating BandISM Sub-GHzISM Sub-GHzISM 2.4-GHzLicensed LTE bandData rate100 bps (DL)0.5 kbps (DL)20 kbps (DL)170 kbps (DL)Down Link (DL)600 bps (UL)50 kbps (UL)80 kbps (UL)250 kbps (UL)Up Link (UL)ModulationUNB-DBPSKCSSRPMA-DSSSQPSKBandwidth125 KHz125 KHz, 250 KHZ,1 MHz180 kHz500 KHzPayload12 B UL, 8 B DLup to 250 B10 KB1600 BLength (Bytes)

LoRa is widely used for LPWAN applications because LoRa networking is an open-source technology that enables autonomous network set-up at low cost. (See M. C. Bor, U. Roedig, T. Voigt, and J. M. Alonso, “Do LoRa low-power wide-area networks scale?” in Proceedings of the International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2016, pp. 59-67, incorporated herein by reference in its entirety). LoRa networks have been widely deployed for many applications and research systems. The openness of LoRa makes it an excellent choice for diverse IoT deployments. (See L. Vangelista, A. Zanella, and M. Zorzi, “Long-range IoT technologies: The dawn of LoRa,” in Proceedings of the Future Access Enablers of Ubiquitous and Intelligent Infrastructures, 2015, pp. 51-58, incorporated herein by reference in its entirety).

General IoT applications include smart buildings, smart cities, smart agriculture, smart meters, and water quality measurement. (See Vangelista et al.; Semtech, “Smart buildings transformed using Semtech's LoRa technology,” April 2017, white paper; Semtech, “Smart cities transformed using Semtech's LoRa technology,” July 2017, white paper; Semtech, “Revolutionising smart agriculture using semtech's LoRa technology,” October 2017, white paper; Semtech, “Real-world LoRaWAN network capacity for electrical metering applications,” September 2017, white paper; and D. Magrin, M. Centenaro, and L. Vangelista, “Performance evaluation of LoRa networks in a smart city scenario,” in Proceedings of the International Conference On Communications, 2017, pp. 1-7, each incorporated herein by reference in their entirety).

LoRa is a physical layer that employs Chirp Spread Spectrum (CSS) modulation. (See LoRa-Alliance; and O. Bernard, A. Seller, and N. Sornin, “Low power long range transmitter,” European Patent EP 2763321 A1, Aug. 6, 2014; 0. Seller and N. Sornin, “Low power long range transmitter,” Aug. 7 2014, US20140219329; G. Ferre and A. Giremus, “LoRa physical layer principle and performance analysis,” in 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), December 2018, pp. 65-68 and A. Berni and W. Gregg, “On the utility of chirp modulation for digital signaling,” IEEE Transactions on Communications, vol. 21, no. 6, pp. 748-751, June 1973, each incorporated herein by reference in their entirety). On top of the LoRa physical layer, long range wide area networks (LoRaWAN) define a simple medium access control (MAC) protocol in which LoRaWAN allows end devices, e.g., sensors, to communicate with a network via gateways. However, this communication is limited by a small duty-cycle (e.g., 1%).

LoRa enables end devices to save energy, and network lifetime is increased. LoRa operates in unlicensed ISM (Industrial, Scientific and Medical) radio bands that are available worldwide. However, usage time of the channels must be limited to a few seconds per day per node, in a fair usage policy. (See Eric B., “LoRa Documentation” ©2018, Powered by Sphinx 1.8.5 & Alabaster 0.7.12, incorporated herein by reference in its entirety). Hence, the main issue in LoRa and LoRaWAN is their throughput limitation, as the indicative physical bitrate varies between 250 and 1000 bps.

Moreover, when two end-devices transmit simultaneously using the same parameters (such as channel and Spreading Factor (SF)) and are received by the gateway with a similar power, a collision occurs and none of the signals are decoded by LoRa. Thus, both end-devices must retransmit, which further reduces their achievable throughput. If one signal has larger received power than the other signals, the capture effect allows this signal to be correctly decoded. Compressive sensing theory allows sparse sources to be sampled below the Nyquist rate and guarantees excellent recovery of the original source signal/data with certain conditions. (See Emmanuel Candès and Michael Wakin, An introduction to compressive sampling.IEEE Signal Processing Magazine,25(2), pp. 21-30, March 2008, incorporated herein by reference in its entirety).

LoRa can be widely used for tracking and location exchange. The nature of kinematic information is known to be sparse, therefore compressive sampling can be used to optimize the transmission of the data at the LoRa devices, and for the recovery of the data at the gateway. Additionally, encoding in compressive sampling is a direct random linear combination which has very low complexity and can be easily implemented on the LoRa devices. Although the recovery at the gateway will require added complexity, a gateway is a powerful fusion center which can collect and analyze the data from multiple LoRa devices.

Accordingly, it is one object of the present disclosure to provide a system for a compressive sensing-based integrated with LoRa communications for IoT devices.

SUMMARY

In an exemplary embodiment, a system for communicating in a low power wide area network (LPWAN) is described, comprising a long range (LoRa) sensing device having circuitry including at least one sensor configured to generate bit stream of sensor signals, a first physical layer (PHY), a first medium access (MAC) layer, a compressive sensing-aided transmission sub-layer embedded within the first medium access layer, a first computer processing unit including a computer-readable medium comprising program instructions, executable by a first processing circuitry, to cause the first processing circuitry to receive, at the first physical layer of a LoRa device, the bit stream of sensor signals at an incoming velocity, encode the bit stream to generate an encoded bit stream, modulate the encoded bit stream by chirp spread spectrum modulation to generate a chirp modulated encoded bit stream, form a first transmission packet including at least a preamble, the chirp modulated encoded bit stream and a cyclic redundancy check (CRC), receive the first transmission packet by the first multiple access layer (MAC) of the LoRa device, compress the chirp modulated encoded bit stream of the first transmission packet into sparse vectors, combine the sparse vectors with a linear combination of a randomly selected set of sparse vectors representing past measurements and the in-coming velocity of the bit stream to generate a payload, form a second transmission packet including the preamble, the payload and the cyclic redundancy check, transmit, by the first physical layer, the second transmission packet at a randomly selected time, a receiver having circuitry including a second physical layer (PHY), a second medium access (MAC) layer, a sparse recovery sub-layer embedded within the second MAC layer, a second computer processing unit including a computer-readable medium comprising program instructions, executable by a second processing circuitry, to cause the second processing circuitry to receive, at the second physical layer, the second transmission packet, de-map the transmission packet to recover a compressed, encoded bit stream, reconstruct, by the sparse recovery sub-layer, the compressed, encoded bit stream by recovering sparse vectors to generate a chirp spread spectrum encoded bit stream, demodulate the chirp spread spectrum encoded bit stream by chirp spread spectrum demodulation to generate an encoded bit stream, decode the encoded bit stream to remove indexing and generate an interleaved bit stream, de-interleave the interleaved bit stream, and decode the interleaved bit stream to recover the bit stream of sensor signals.

In another exemplary embodiment, a method is described for compressive sensing based communications of low range (LoRa) devices in a low power wide area network (LPWAN), comprising receiving, by a physical layer of a LoRa device, a bit stream of sensor measurements at an incoming velocity, encoding the bit stream to generate an encoded bit stream, modulating the encoded bit stream by chirp spread spectrum modulation to generate a chirp modulated encoded bit stream, forming a first transmission packet including at least a preamble, the chirp modulated encoded bit stream and a cyclic redundancy check (CRC), receiving the first transmission packet by a multiple access layer (MAC) of the LoRa device, compressing the chirp modulated encoded bit stream of the first transmission packet into sparse vectors, combining the sparse vectors with a linear combination of a randomly selected set of sparse vectors representing past measurements and the in-coming velocity of the bit stream to generate a payload, forming a second transmission packet including the preamble, the payload and the cyclic redundancy check, transmitting the second transmission packet at a randomly selected time.

In another exemplary embodiment, a method for decoding compressive sensing based communications of low range (LoRa) devices in a low power wide area network (LPWAN), comprising receiving, at a receiver physical layer, a compressed, encoded transmission packet from a LoRa device, de-mapping the transmission packet to recover a compressed, encoded bit stream, reconstructing, by a sparse recovery sub-layer of a receiver multiple access layer (MAC) layer, the encoded transmission by recovering sparse vectors to generate a chirp spread spectrum encoded bit stream, demodulating the chirp spread spectrum encoded bit stream by chirp spread spectrum demodulation to generate an encoded bit stream, decoding the encoded bit stream to remove indexing and generate an interleaved bit stream, de-interleaving the interleaved bit stream, and decoding the interleaved bit stream to recover the bit stream of the communications.

The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure, and are not restrictive.

