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. 1. Introduction A typical sensor network comprises a large number of multifunctional, low-cost, and low-power nodes that are deployed densely and randomly in an environment for monitored sensing to control the environment, perform local processing, and communicate results with a base station that performs most of the complex processing. One of the many challenges concerning wireless sensor networks (WSNs) is how to provide Quality of Service (QoS) parameter guarantees in real-time applications. Several approaches and protocols have been proposed in the literature for QoS parameter support in these types of networks [1, 2]. Energy consumption is considered to be the most important constraint in WSNs because of the low power and the processing factors. These factors reduce the QoS and the lifetime of the network. The primary concern is how to properly use resources (for deriving multimedia content) to provide appropriately shared data among all of the transmission radios while maintaining a proper level of imaging and video data transmission. The main goal is the appropriate use of multimedia resources by properly maintaining a level of optimized QoS, which further depends on the performance of the
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