Low-Power Wide-Area Network Over White Spaces

Saifullah, Abusayeed; Rahman, M.; Ismail, Dali; Lu, Chenyang; Liu, Jie; Chandra, Ranveer

doi:10.1109/tnet.2018.2856197

Cited by 46 publications

(61 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In regard to the white space networking, the closest work to ours is [36] which considers multiple WiFi-like networks in white spaces, where all users have access to white space database, and every access point (AP) chooses a single channel, the problem thus is different from our integration. Here we provide a brief overview of the design and architecture of a single SNOW that we developed in [2]- [4]. SNOW is an asynchronous, long range, low power WSN platform to operate over TV white spaces.…”

Section: Related Workmentioning

confidence: 99%

“…which means that the probability of satisfying Constraints (2) and (3) is ≈ 1 even if step 2 runs. Thus, the probability of satisfying all constraints is ≈ 1.…”

Section: If Step 2 Runs Thenmentioning

confidence: 99%

“…Due to their escalating demand, LPWANs are gaining momentum, with multiple competing technologies being developed including LoRaWAN, SigFox, IQRF, RPMA (Ingenu), DASH7, Weightless-N/P in the ISM band; and EC-GSM-IoT, NB-IoT, LTE Cat M1 (LTE-Advanced Pro), and 5G in the licensed cellular band (see survey [1]). In parallel, to avoid the crowd of the limited ISM band and the cost of the licensed band, we developed SNOW (Sensor Network Over White Spaces), an LPWAN architecture to support wide-area WSN by exploiting the TV white spaces [2]- [4]. White spaces refer to the allocated but locally unused TV channels, and can be used by unlicensed devices as secondary users.…”

Section: Introductionmentioning

confidence: 99%

“…Thanks to their lower frequencies (54-862MHz in the US), white spaces have excellent propagation characteristics over long distance and obstacles. While their potentials have been explored mostly for broadband access (see survey [5]), our design and experimentation demonstrated the potential of SNOW to enable asynchronous, low power, bidirectional, and massively concurrent communications between numerous sensors and a base station (BS) directly over long distances [2]- [4].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage

Rahman

Saifullah

2020

IEEE/ACM Trans. Networking

Self Cite

View full text Add to dashboard Cite

Low-Power Wide-Area Networks (LPWANs) are evolving as an enabling technology for Internet-of-Things (IoT) due to their capability of communicating over long distances at very low transmission power. Existing LPWAN technologies, however, face limitations in meeting scalability and covering very wide areas which make their adoption challenging for future IoT applications, especially in infrastructure-limited rural areas. To address this limitation, in this paper, we consider achieving scalability and extended coverage by integrating multiple LPWANs. SNOW (Sensor Network Over White Spaces), a recently proposed LPWAN architecture over the TV white spaces, has demonstrated its advantages over existing LPWANs in performance and energyefficiency. In this paper, we propose to scale up LPWANs through a seamless integration of multiple SNOWs which enables concurrent inter-SNOW and intra-SNOW communications. We then formulate the tradeoff between scalability and inter-SNOW interference as a constrained optimization problem whose objective is to maximize scalability by managing white space spectrum sharing across multiple SNOWs. We also prove the NPhardness of this problem. To this extent, We propose an intuitive polynomial-time heuristic algorithm for solving the scalability optimization problem which is highly efficient in practice. For the sake of theoretical bound, we also propose a simple polynomialtime 1 2 -approximation algorithm for the scalability optimization problem. Hardware experiments through deployment in an area of (25x15)km 2 as well as large scale simulations demonstrate the effectiveness of our algorithms and feasibility of achieving scalability through seamless integration of SNOWs with high reliability, low latency, and energy efficiency.Index Terms-Low-power wide-area network, white spaces, sensor network.

show abstract

Section: Related Workmentioning

confidence: 99%

“…which means that the probability of satisfying Constraints (2) and (3) is ≈ 1 even if step 2 runs. Thus, the probability of satisfying all constraints is ≈ 1.…”

Section: If Step 2 Runs Thenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage

Rahman

Saifullah

2020

IEEE/ACM Trans. Networking

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wireless Broadband Access Protocols Waldo [56] FIWEX [57] SenseLess [58] V-Scope [59] HySIM [60] WISER [8] KNOWS [13] WINET [14] SNOW [12] CR-S [61] WhiteFi [3] WhiteNet [62] TVWS Access Its detection technique leverages locally measured signal features and location. In short, it exploits the crowdsourced local spectrum measurements data from multiple low-cost sensing devices.…”

Section: Tvws Network Architecturesmentioning

confidence: 99%

A comprehensive survey on networking over TV white spaces

Rahman

Saifullah

2019

Pervasive and Mobile Computing

Self Cite

View full text Add to dashboard Cite

TV white spaces refer to the allocated but locally unused TV spectrum and can be used by unlicensed devices as secondary users. Thanks to their lower frequencies (e.g., 54 -698 MHz in the US), communication over the TV spectrum has excellent propagation characteristics over long distances and through obstacles. These characteristics along with their wide availability make the TV white spaces a great choice and alternative to many existing wireless technologies, especially the ones that need long range and high bandwidth communication. In the last decade, there have been numerous efforts from academia, industries, and standards bodies for exploiting the potentials of the TV white spaces for several applications including wireless broadband Internet access.Their characteristics and features also hold potentials for many new applications including sensing and monitoring, Internet of Things, wireless control, smart utility, location-based services, and transportation and logistics. In this paper, we perform a retrospective review and comparative study of existing work on networking in the TV white spaces. Additionally, we discuss the associated research challenges such as dealing with the interference between primary and secondary TV spectrum users, TV white space temporal and spatial variations and fragmentation, antenna design, mobility, and security. We also describe the future research directions to handle the above challenges. To the best of our knowledge, this is the first comprehensive survey on the literature of TV white space networking research.

show abstract

Cognitive Long‐range: Towards efficient public communication infrastructure for Internet of Things

Mousavi

Khademzadeh

Rahmani

2022

Int J Communication

View full text Add to dashboard Cite

Summary The most common Internet of Things (IoT) scenarios entail devices with limited energy resources and need to be connected to the Internet via wireless networks. This has driven the recent development of low‐power wide‐area networks (LPWANs) and the rise of the Long Range (LoRa) technology. The LoRa protocol has a simple modulation scheme that ensures low power consumption, high convergence, and resistance against interference. In most LPWAN technologies, several physical layer challenges arise, such as low data rates, spectral inefficiency, and increased interference. As a physical layer solution, the cognitive radio (CR) offers a possible way of resolving these challenges. CR allows wireless networks to operate without the need for a dedicated spectrum. Regarding the variety of end‐user requirements, developing a public communication network that can support such diverse and heterogeneous applications is necessary to reduce the implementation costs than developing a dedicated communication network for each application. This paper proposes a Cognitive LoRa (C‐LoRa) protocol that utilizes unlicensed and licensed frequencies as well as interference mitigation to improve the QoS of LoRa. To extract the priority list of traffic patterns, C‐LoRa incorporates the Analytic Hierarchy Process (AHP) algorithm. The priority list enables real‐time applications to receive optimal spectrum allocation. C‐LoRa can be efficiently implemented as a public communication infrastructure for heterogeneous IoT devices. The addition of licensed channels improves the overall QoS and decreases the average waiting time in queues. The platform layer of C‐LoRa consists of a cognitive engine that sends traffic priority lists to cognitive spectrum allocators. The IoT application servers are connected to the cloud platform layer via SNMP, HTTP, and other desired protocols. Access gateways equipped with a cognitive spectrum allocator are always connected to a power supply and serve as a transparent bridge to the cognitive engine at the platform layer, converting RF packets to IP packets and vice versa.

show abstract

Low-Power Wide-Area Network Over White Spaces

Cited by 46 publications

References 18 publications

Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage

Integrating Low-Power Wide-Area Networks for Enhanced Scalability and Extended Coverage

A comprehensive survey on networking over TV white spaces

Cognitive Long‐range: Towards efficient public communication infrastructure for Internet of Things

Contact Info

Product

Resources

About