Abstract
This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines.
Highlights
The growing need to support high performance applications in multi-hop multimedia wireless sensor networks (MWSNs) [1] while coping with limited node capabilities [2] highlights the necessity of resource sharing and node collaboration [3,4,5]
Two classic heuristic algorithms and a conventional genetic algorithm (GA)-based algorithm are picked as benchmark competitors: Greedy [16]: The Greedy algorithm assigns most of the tasks to a powerful node, e.g., the gateway
Dynamic Task Allocation and Scheduling (DTAS) shows the best performance compared to MTMS, ITAS and Greedy, it is inevitable that the deadline miss ratio of DTAS increases significantly when more link-change events take place
Summary
The growing need to support high performance applications in multi-hop multimedia wireless sensor networks (MWSNs) [1] while coping with limited node capabilities [2] highlights the necessity of resource sharing and node collaboration [3,4,5]. In a surveillance sensor network consisting of wireless camera nodes [6,7], real-time computation of large amounts of visual data and performing complex image processing-based algorithms in resource constrained sensor nodes imposes news challenges for MWSN design. When a critical agent/node leaves the network, due to communication interruption or physical node failure, serious consequences, such as network service disruption, can occur In such cases, control messages are exchanged among nodes in order to isolate the faulty ones and detect the affected tasks that need to be immediately reallocated to suitable nodes. Stochastic movements of a patrolling agent might affect its own communication or cause interference on its neighbours This implies that the effectiveness of a fixed task allocation solution may degrade and eventually become invalid if there is no update for the solution based on the latest network conditions. Due to the complexity of MWSNs, assessments of finding a qualified solution are often computationally time consuming, which has a direct effect on the quality of the computed solution for time-critical applications
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
