Traffic aware field-based routing for wireless sensor networks

Abstract

Route estimation process often involves significant message exchanges among wireless sensor nodes while selecting the least cost path. Nodes along this path handle more traffic that leads to death of battery powered nodes and shortening network life. Thus, routing mechanisms for wireless sensor network (WSN) must be traffic aware and at the same time, the alternate route(s) incur less delay overhead. This paper considers a query-driven application scenario in WSN where the sink diffuses query over the network to fetch information. A novel field-based routing (FBR) mechanism is proposed that inherits the physical properties of Coulomb's law for point charges in free space. It defines a distance field parameter corresponding to each sensor node with respect to the sink. The sink being the negatively charged particle the packets from sensor nodes (positively charged particle) flow towards the field generating sink. The algorithm considers energy depletion rate for estimating the virtual potential field at nodes so as to avoid the nodes having less remaining energy. Further, the gradient updation is based on the local information which results in less message complexity (O(n)) and low computation overhead, which is comparable to the best available approaches. NS-2 based simulation demonstrates a significant enhancement in network lifetime, increased packet reception ratio and reduction in energy dissipation rate making the FBR mechanism suitable for WSN.

RETRACTED ARTICLE: Multivariate weighted isotonic regressive modest adaptive boosting-based resource-aware routing in WSN

“…However, efficient routing approaches were not considered to extend the network lifetime. FBR technique is designed in [8] for minimizing computation overhead and increase the delivery ratio. However, it failed to evaluate performance of FBR method with mobile node scenarios.…”

In-Depth Study of Potential-Based Routing and New Exploration of Its Scheduling Integration

“…Nevertheless, they commonly assign scalar values to nodes to form a field used as guidance for routing, with packets traversing to destinations following the steepest gradient under the field. For example, in [13,14], mechanisms for wireless sensor networks (WSNs) consider both the distance to the sink and the energy factor, drawing inspiration from Coulomb's law and force concept, respectively. A similar study in [15] targets WMNs in underground mines.…”

Neural Network Clustering and Swarm Intelligence‐Based Routing Protocol for Wireless Sensor Networks: A Machine Learning Perspective

“…Using SVM as the goal, Anand et al [ 22 ] designed a decision function based on statistical learning theory. This decision function has a simple implementation at the cluster nodes to detect abnormal sensors since it allows for low-resolution detection.…”