Set-covering-based algorithm for delay constrained relay node placement in Wireless Sensor Networks

Abstract

The Delay Constrained Relay Node Placement (DCRNP) problem in Wireless Sensor Networks (WSNs) aims to deploy minimum relay nodes such that for each sensor node there is a path connecting this sensor node to the sink without violating delay constraint. As WSNs are gradually employed in time-critical applications, the importance of the DCRNP problem becomes noticeable. For the NP-hard nature of DCRNP problem, an approximation algorithm-Set-Covering-based Relay Node Placement (SCA) is proposed to solve the DCRNP problem in this paper. The proposed SCA algorithm deploys relay nodes iteratively from sink to the given sensor nodes in hops, i.e., in the kth iteration SCA deploys relay nodes at the locations that are k hops apart from the sink. Specifically, in each iteration, SCA first finds the candidate deployment locations located within 1 hop to the relay nodes and sensor nodes, which have already been connected to the sink. Then, a subset of these candidate deployment locations, which can guarantee the existence of paths connecting unconnected sensor nodes to the sink within delay constraint, is selected to deploy relay nodes based on the set covering method. As the iteration of SCA algorithm, the sensor nodes are gradually connected to the sink with satisfying delay constraint.The elaborated analysis of the approximation ratio of SCA algorithm is given out, and we also prove that the SCA is a polynomial time algorithm through rigorous time complexity analysis. To evaluate the performance of the proposed SCA algorithm, extensive simulations are implemented, and the simulation results show that the SCA algorithm can significantly save the deployed relay nodes comparing to the existing algorithms, i.e., at most 31.48% deployed relay nodes can be saved due to SCA algorithm.Index Terms-Wireless sensor networks, set covering, delay constrained relay node placement, approximation algorithm.• An approach is designed to represent each CDL or SN by the unconnected SNs, which can be connected to the sink

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

“…In [14], [15], optimal relay placements and device-relay-channel associations were determined to achieve energy efficient uplink transmissions of IoT devices. The relay placement problem under a simplified delay constraint was investigated in [16]. Nevertheless, the above relay research works concentrated on static network states.…”

A practical data driven approach for the deployment of WSNs on realistic terrains

“…The proposed methods for the deployment of RNs differ first and foremost in their objectives. As such, some of these methods aim to ensure only simple connectivity (1‐connectivity) . Other methods aim to reach k ‐connectivity ( k ≥ 2), which means, in the context of the problem at hand, that each SN has k disjoint paths to route its own data to the CNs, allowing the fault‐tolerance.…”

“…Other methods aim to reach k ‐connectivity ( k ≥ 2), which means, in the context of the problem at hand, that each SN has k disjoint paths to route its own data to the CNs, allowing the fault‐tolerance. The connectivity objective is supported by two deployment modes: (i) reactive deployment, initiated after the loss of connectivity to re‐establish it, and (ii) proactive deployment, adopted to avoid any loss of connectivity …”

“…In addition to connectivity, these methods aim to ensure other objectives, such as minimizing the cost, by minimizing the number deployed RNs, minimizing end‐to‐end delivery delay as well as the maximization of the network lifetime, through the preservation of the SNs energy and the RNs energy, or by maximizing the quality of communication links, which helps to minimize packet retransmissions. It is useful to specify that the communication links are modeled in these methods by binary, or probabilistic models, as a function of many parameters, which include among others the Euclidean distance between the communicating nodes, their transmission range (power), the obstacles, and the transmission medium (acoustic, radio, etc) …”

Delay constrained relay node placement in two-tiered wireless sensor networks: A set-covering-based algorithm

“…Taking the factory automation as an example, the data sensed by SNs is typically time-sensitive, such as alarm notification and information for feedback control, and thus the importance of receiving the data at the sink in a timely manner is noticeable (Liang et al, 2011;Zheng et al, 2015). However, the literature about the DCRNP problem are very limited Kumar, 2010, 2014;Nigam and Agarwal, 2014;Ma et al, 2016b;Sitanayah et al, 2014;Ma et al, 2016c). Kumar, (2010, 2014) first prove that the DCRNP problem is NP-hard, and a Shortest Path Tree based Iterative Relay Pruning (SPTiRP) algorithm is proposed.…”