IEGGR: Improved Energy-aware and Delivery Guarantee Geographic Routing

Abstract

Wireless Sensor Networks are a set of sensor nodes that probe their surroundings and send their data over multi-hop routes to a base-station (BS) for processing. Many routing protocols have been developed for WSNs. A great attention has been given to geographic routing protocols, which rely only on local knowledge of nodes to disseminate data towards the BS. In this paper, we propose a new energy efficient geographic routing protocol called Improved Energy-aware and delivery Guarantee Geographic Routing protocol (IEGGR). The proposed protocol exploits the positions of neighboring nodes, which are closest to the BS to forward packets through the shortest path in terms of energy consumption. IEGGR introduces a mechanism in order to forward packets efficiently around voids. IEGGR algorithm considers the WSN as a connected-graph and to route a packet from a source node to the BS, IEGGR tries making the locally optimal choice at each intermediate node with the hope of finding a global optimum path, which ai ms to minimize energy consumption. To find greedily the optimal path, each forwarding node exploits its neighbor positions that are closest to the BS in order to iteratively building the end-to-end optimal path. Simulation results show that IEGGR is efficient in energy management during packet forwarding.

Location Centric Energy Harvesting Aware Routing Protocol for IoT in Smart Cities

“…Redjimi et al [20] presented a location-based IEGGR (Incremental Expansion Greedy Geographic Routing) for solving the local minima problem. The main idea behind IEGGR is to construct a local sub-graph for each node, known as the Routing Area, by including neighbors that are closer to the base station than the source node.…”

“…This study presents a routing protocol that considers energy harvesting when choosing an efficient link for transmitting data to a specific destination. Past studies have explored various techniques for selecting the optimal link, including location or angle-based methods [18]- [20], [23], [28], and cost metrics-based approaches [9]- [14], [17]. However, these methods suffer from several issues, such as problems with selecting the optimal intermediate node, difficulties in maintaining routes, energy wastage, and increased communication complexities.…”

“…If there are no neighbors, it will use the broadcast mechanism to transmit the initialization packet (lines 4-5). If the source node has neighbors, it will first calculate the angle between the source node and the destination node (lines 7-12), then it will find the closest angle to the destination among the neighbor nodes' angles (lines [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Now, when a node receives the initialization packet, it will check whether it is the destination node (lines 28-29).…”