Approximation Algorithms for Constrained Relay Node Placement in Energy Harvesting Wireless Sensor Networks

Abstract

The constrained relay node placement problem in a wireless sensor network seeks the deployment of a minimum number of relay nodes (RNs) in a set of candidate locations in the network to satisfy specific requirements, such as connectivity or survivability. In this paper, we study the constrained relay node placement problem in an energy-harvesting network in which the energy harvesting potential of the candidate locations are known a priori. Our aim is to place a minimum number of relay nodes, to achieve connectivity or survivability, while ensuring that the relay nodes harvest large amounts of ambient energy. We present the connectivity and survivability problems, discuss their NP-hardness, and propose polynomial time O(1)-approximation algorithms with low approximation ratios to solve them. We validate the effectiveness of our algorithms through numerical results to show that the RNs placed by our algorithms harvest 50% more energy on average than those placed by the algorithms unaware of energy harvesting. We also develop a unified-mixed integer linear program (MILP)-based formulation to compute a lower bound of the optimal solution for minimum relay node placement and demonstrate that the results of our proposed algorithms were on average within 1.5 times of the optimal.

Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks

“…Misra et al [33] ensured connectivity by deploying a minimum number of RNs in ST-WSNs, where the routers were constrained to be placed at a subset of candidate locations, this is the so-called Constrained RNPP (C-RNPP). Following this constrained approach, Misra et al [34] placed a minimum number of routers to ensure connectivity and survivability in energy-harvesting ST-WSNs, where the energy harvesting potential of the candidate locations was known a priori. Nigam et al [35] proposed a branch-and-cut algorithm to deploy the minimum number of routers at a subset of candidate locations in ST-WSNs, such that the sensors were communicated to the sink node within a prespecified delay bound.…”

“…This is not the case for heuristics, providing a unique solution. As described above, many works assumed heuristics for ST-WSNs [3,[28][29][30][31][32][33][34][35]. However, only a few papers studied the single-tiered approach by assuming metaheuristics [36,37].…”

Rainbow product ranking based relay placement and adaptive retransmission scheme for a reliable 802.15.4e LLDN

“…To decide upon which block codes to be used as FEC technique, RS (15,11) and RS (15,7) were chosen as candidates based on [13]. To incorporate any FEC technique we must ensure that it adheres to the requirements of the standard.…”

“…As RS (15,11) block code fulfils the requirement of supporting more than 100 sensors per coordinator [3] which is suitable for IEEE 802.15.4 based IWSNs [13], hence it is chosen as the FEC technique for retransmission scheme. 3) Relay+FEC: This scheme incorporates RS (15,11) while relaying from relay-to-coordinator only when uncoded transmission between sensor-to-coordinator fails. Fig.…”

“…The number of relays also plays a crucial role in determining the amount of energy consumed. Minimizing the number of relays in a network satisfying a specific requirement such as energy efficiency, connectivity, energy harvesting [11] and abiding by certain physical constraints [12] has also been a topic of research in recent years. However, they do not focus on reliability of IWSNs.…”