| Title | Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications |
|---|---|
| ID_Doc | 41957 |
| Authors | Choudhury, N; Gupta, M; Nasralla, MM; Fujita, S |
| Title | Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications |
| Year | 2023 |
| Published | |
| Abstract | With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF). |
Similar Articles
| ID | Score | Article |
|---|---|---|
41922
|
El-Aasser, M; Badawi, R; Ashour, M; Elshabrawy, T Examining Carrier Sense Multiple Access to Enhance LoRa IoT Network Performance for Smart City Applications(2019) | |
| 37908
|
Sandoval, RM; Garcia-Sanchez, AJ; Garcia-Haro, J Performance optimization of LoRa nodes for the future smart city/industry(2019)Eurasip Journal On Wireless Communications And Networking, 2019, 1 | |
| 38744
|
Ikpehai, A; Adebisi, B; Anoh, K Effects of Traffic Characteristics on Energy Consumption of IoT End Devices in Smart City(2018) | |
| 38365
|
Philip, MS; Singh, P Enhancing Energy Efficiency in LoRaWAN System for Smart City Applications(2023)Ieee Sensors Letters, 7, 12 | |
| 37128
|
Dawaliby, S; Bradai, A; Pousset, Y Joint slice-based spreading factor and transmission power optimization in LoRa smart city networks(2021) | |
| 41768
|
Orsino, A; Araniti, G; Militano, L; Alonso-Zarate, J; Molinaro, A; Iera, A Energy Efficient IoT Data Collection in Smart Cities Exploiting D2D Communications(2016)Sensors, 16, 6 | |
| 45505
|
Sanchez-Gomez, J; Gallego-Madrid, J; Sanchez-Iborra, R; Skarmeta, A Performance Study of LoRaWAN for Smart-City Applications(2019) | |
| 41550
|
Aksoy, A; Yildiz, O; Karlik, SE Comparative Analysis of End Device and Field Test Device Measurements for RSSI, SNR and SF Performance Parameters in an Indoor LoRaWAN Network(2024)Wireless Personal Communications, 134, 1 | |
| 40560
|
Magrin, D; Centenaro, M; Vangelista, L Performance Evaluation of LoRa Networks in a Smart City Scenario(2017) | |
| 43366
|
Ogbodo, EU; Abu-Mahfouz, AM; Kurien, AM Enabling LPWANs for Coexistence and Diverse IoT Applications in Smart Cities Using Lightweight Heterogenous Multihomed Network Model(2022)Journal Of Sensor And Actuator Networks, 11, 4 |