scholarly journals Optimized Quality of Service for Real-Time Wireless Sensor Networks Using a Partitioning Multipath Routing Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammed Zaki Hasan ◽  
Tat-Chee Wan
Keyword(s):  
Mathematical Model ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Real Time ◽  
Resource Sharing ◽  
Mixed Integer ◽  
Critical Parameters ◽  
End To End

Multimedia sensor networks for real-time applications have strict constraints on delay, packet loss, and energy consumption requirements. For example, video streaming in a disaster-management scenario requires careful handling to ensure that the end-to-end delay is within the acceptable range and the video is received properly without any distortion. The failure to transmit a video stream effectively occurs for many reasons, including sensor function limitations, excessive power consumption, and a lack of routing reliability. We propose a novel mathematical model for quality of service (QoS) route determination that enables a sensor to determine the optimal path for minimising resource use while satisfying the required QoS constraints. The proposed mathematical model uses the Lagrangian relaxation mixed integer programming technique to define critical parameters and appropriate objective functions for controlling the adaptive QoS constrained route discovery process. Performance trade-offs between QoS requirements and energy efficiency were simulated using the LINGO mathematical programming language. The proposed approach significantly improves the network lifetime, while reducing energy consumption and decreasing average end-to-end delays within the sensor network via optimised resource sharing in intermediate nodes compared with existing routing algorithms.

scholarly journals Improving Quality-of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

2019 ◽  
Author(s):  
Eduardo Sallum ◽  
Nuno Pereira ◽  
Mario Alves ◽  
Max Mauro Santos
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Low Power ◽  
Fine Tuning ◽  
Wide Area ◽  
Mixed Integer ◽  
Wide Area Networks ◽  
Dynamic Allocation ◽  
Equal Distribution

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

scholarly journals Improving Quality-of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

2019 ◽  
Author(s):  
Eduardo Sallum ◽  
Nuno Pereira ◽  
Mário Alves ◽  
Max Santos
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Low Power ◽  
Fine Tuning ◽  
Wide Area ◽  
Mixed Integer ◽  
Wide Area Networks ◽  
Dynamic Allocation ◽  
Equal Distribution

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology which uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, considering realistic scenarios. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 30% in DER, and a number of collisions 17 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 10.5% and 4% of DER, and a number of collisions 13.5 and 7.5 times smaller than equal-distribution and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption 3.6 and 2.74 times lower than the equal-distribution and random dynamic allocation policies, respectively. Furthermore, we approach the practical aspects on how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

scholarly journals Improving Quality-Of-Service in LoRa Low-Power Wide-Area Networks through Optimized Radio Resource Management

2020 ◽  
Vol 9 (1) ◽  
pp. 10 ◽  
Author(s):  
Eduardo Sallum ◽  
Nuno Pereira ◽  
Mário Alves ◽  
Max Santos
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
Low Power ◽  
Fine Tuning ◽  
Wide Area ◽  
Mixed Integer ◽  
Wide Area Networks ◽  
Dynamic Allocation ◽  
Equal Distribution

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard—LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRaWAN networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova’s (SOTA), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova’s, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

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