Energy and congestion aware routing based on hybrid gradient fields for wireless sensor networks

Abstract

The principles of physics and system sciences are increasingly used in the field of network engineering to design network protocols. This work proposes an energy and congestion aware routing algorithm inheriting the concepts of potential field. It uses depth and time variant network parameters for forwarding the data packets through low congestion and energy balanced path. We defines a novel forward aware energy density as decision metric along with residual energy and queue-length for forwarding data packets. This results in network wide balanced residual energy and enhanced network lifetime. The proposed mechanism is evaluated for the transmission rounds before the first dead node (FDN) is detected. It was found that in typical traffic conditions there was an average increment of 45% transmission rounds till the FDN appeared. Moreover, the simulated and theoretical findings are compared using statistical measures to justify the proposed mechanism energy and congestion awareness.

RPR: Reliable path routing protocol to mitigate congestion in critical IoT applications

“…In this algorithm, the selection of the next-hop node is based on the signal-to-interference-noise-ratio of the link, the survivability factor of the path, and the path loss distance. Jain et al [12] introduced the Energy and Congestion Aware Routing (ECAR) based on hybrid gradient fields to reduce the congestion in the network. In this method, the packet is forwarded based on the depth, residual energy, forward aware residual energy density, and the queue length of the node.…”

Intelligent Service Selection in a Multi-Dimensional Environment of Cloud Providers for Internet of Things Stream Data through Cloudlets

“…Using the architecture expressed in fog-cloud, the energy optimization of both cloud and fog layers can be managed simultaneously to contribute to the IoT development goal [20]. Fog computing architecture involves three main layers: IoT devices as the first layer, the fog layer as the second layer, and finally, the cloud layer [8,15].…”

“…Therefore, the distribution of data among different services satisfies the QoS. By distributing data among services, failure in one service does not lead to loss of all data [8].…”

“…The goal is to minimize all five objects in the problem (8). No other solution cannot dominate a solution in the Pareto.…”

“…According to data released in 2014, the American data centers consumed 70 billion KW/h of Electrical energy. Therefore, there is a lot of research focusing on energy consumption [6][7][8]. On the other side, building energy efficiency and estimating building occupancy information effectively in real time as well as reducing the energy consumption in non-residential buildings are studied widely in the literature [9,10].…”