Abstract
A wireless sensor network (WSN) is composed of a large number of tiny sensor nodes. Sensor nodes are very resource-constrained, since nodes are often battery-operated and energy is a scarce resource. In this paper, a resource-aware task scheduling (RATS) method is proposed with better performance/resource consumption trade-off in a WSN. Particularly, RATS exploits an adversarial bandit solver method called exponential weight for exploration and exploitation (Exp3) for target tracking application of WSN. The proposed RATS method is compared and evaluated with the existing scheduling methods exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), and cooperative reinforcement learning (CRL), in terms of the tracking quality/energy consumption trade-off in a target tracking application. The communication overhead and computational effort of these methods are also computed. Simulation results show that the proposed RATS outperforms the existing methods DIRL and RL in terms of achieved tracking performance.
Highlights
Wireless sensor networks (WSNs) [1] are an important and attractive platform for various pervasive applications like target tracking, area monitoring, routing, and in-network data aggregation
7.1 Simulation environment The proposed method is implemented and evaluated with other task scheduling methods using a WSN multi-target tracking scenario implemented in a C# simulation environment
A given number of sensor nodes are placed randomly in this area which can result in partially overlapping field of view (FOV) of the nodes
Summary
Wireless sensor networks (WSNs) [1] are an important and attractive platform for various pervasive applications like target tracking, area monitoring, routing, and in-network data aggregation. Resource awareness is an important issue for WSNs. Basically, battery power, memory, and processing functionality form the resource infrastructure. A WSN has its own resource and design constraints. Resource constraints include a limited energy, low bandwidth, limited processing capability of the central processing unit, limited storing capacity of the storage device, and short communication range. Design constraints are application-dependent and depend on the environment being monitored. The environment acts as a major determinant regarding the size of the network, deployment strategy, and network topology. The number of sensor nodes or the size of the network changes based on the monitored environment. In indoor environments, fewer nodes are needed to form
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