Optimal placement of relay nodes over limited positions in wireless sensor networks

This paper tackles the challenge of optimally placing relay nodes (RNs) in wireless sensor networks given a limited set of positions. The proposed solution consists of: 1) the usage of a realistic physical layer model based on a Rayleigh block-fading channel; 2) the calculation of the signal-to-interference-plus-noise ratio (SINR) considering the path loss, fast fading, and interference; and 3) the usage of a weighted communication graph drawn based on outage probabilities determined from the calculated SINR for every communication link. Overall, the proposed solution aims for minimizing the outage probabilities when constructing the routing tree, by adding a minimum number of RNs that guarantee connectivity. In comparison to the state-of-the art solutions, the conducted simulations reveal that the proposed solution exhibits highly encouraging results at a reasonable cost in terms of the number of added RNs. The gain is proved high in terms of extending the network lifetime, reducing the end-to-end- delay, and increasing the goodput

Optimal Placement of Relay Stations in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a collection of nodes organized into a cooperative network with sensing, processing and transmitting capabilities. WSNs are becoming an increasingly prominent technology that can be used in diverse application areas. In WSNs, cooperative relay stations are projected as one of the most cost effective solutions to meet the demanding requirement of capacity enhancement. In this paper, major concerns of the wireless sensor networks addressed are optimizing the number of relay stations required for covering the desired percentage of sensor nodes by optimal placement of relay stations and optimal assignment of the sensors to the relay stations. The joint problem of relay station placement and coverage is formulated into a mixed integer program which is solvable by commercial GAMS software with Xpress-MP Solver. Sensitivity analysis is carried out, along with a case study to demonstrate the performance gain of the model

One-step approach for two-tiered constrained relay node placement in wireless sensor networks

© 2012 IEEE. We consider in this letter the problem of constrained relay node (RN) placement where sensor nodes must be connected to base stations by using a minimum number of RNs. The latter can only be deployed at a set of predefined locations, and the two-Tiered topology is considered where only RNs are responsible for traffic forwarding. We propose a one-step constrained RN placement (OSRP) algorithm which yields a network tree. The performance of OSRP in terms of the number of added RNs is investigated in a simulation study by varying the network density, the number of sensor nodes, and the number of candidate RN positions. The results show that OSRP outperforms the only algorithm in the literature for two-Tiered constrained RNs placement

ON RELAY NODE PLACEMENT PROBLEM FOR SURVIVABLE WIRELESS SENSOR NETWORKS

Wireless sensor networks are widely applied to many fields such as animal habitat monitoring, air traffic control, and health monitoring. One of the current problems with wireless sensor networks is the ability to overcome communication failures due to hardware failure, distributing sensors in an uneven geographic area, or unexpected obstacles between sensors. One common solution to overcome this problem is to place a minimum number of relay nodes among sensors so that the communication among sensors is guaranteed. This is called Relay Node Placement Problem (RNP). This problem has been proved as NP-hard for a simple connected graph. Therefore, many algorithms have been developed based on Steiner graphs. Since RNP for a connected graph is NP-hard, the RNP for a survivable network has been conjectured as NP-hard and the algorithms for a survivable network have also been developed based on Steiner graphs. In this study, we show the new approximation bound for the survivable wireless sensor networks using the Steiner graphs based algorithm. We prove that the approximation bound is guaranteed in an environment where some obstacles are laid, and also propose the newly developed algorithm which places fewer relay nodes than the existing algorithms. Consequently, the main purpose of this study is to find the minimum number of relay nodes in order to meet the survivability requirements of wireless sensor networks

Planning the deployment of multiple sinks and relays in wireless sensor networks

Wireless sensor networks are subject to failures. Deployment planning should ensure that when a data sink or sensor node fails, the remaining network can still be connected, and so may require placing multiple sinks and relay nodes in addition to sensor nodes. For network performance requirements, there may also be path-length constraints for each sensor node. We propose four algorithms, Greedy-MSP and GRASP-MSP to solve the problem of multiple sink placement, and Greedy-MSRP and GRASP-MSRP for the problem of multiple sink and relay placement. Greedy-MSP and GRASP-MSP minimise the deployment cost, while ensuring that each sensor node in the network is double-covered, i.e. it has two length-constrained paths to two sinks. Greedy-MSRP and GRASP-MSRP deploys sinks and relays to minimise the deployment cost and to guarantee that all sensor nodes in the network are double-covered and noncritical. A sensor node is noncritical if upon its removal, all remaining sensor nodes still have length-constrained paths to sinks. We evaluate the algorithms empirically and show that these algorithms outperform the closely-related algorithms from the literature for the lowest total deployment cost

Multiple sink and relay placement in wireless networks

Wireless sensor networks are subject to failures. Deployment planning should ensure that when a sink or sensor node fails, the remaining network can still be connected, and so may require placing multiple sinks and relay nodes in addition to sensors. For network performance requirements, there may also be path-length constraints for each sensor node. We propose two local search algorithms, GRASP-MSP and GRASP-MSRP, to solve the problem of multiple sink placement and the problem of multiple sink and relay placement, respectively. GRASP-MSP minimises the deployment cost, while ensuring that each sensor node in the network is double-covered, i.e. it has two length-constrained paths to two sinks. GRASP-MSRP deploys sinks and relays to minimise the deployment cost and to guarantee that all sensor nodes in the network are double-covered and noncritical. A sensor node is noncritical if upon its removal, all remaining sensor nodes still have length-constrained paths to sinks. We evaluate the algorithms empirically and show that both GRASP-MSP and GRASP-MSRP outperform the closely-related algorithms from the literature for the lowest total deployment cost

Magnetworks: how mobility impacts the design of Mobile Networks

In this paper we study the optimal placement and optimal number of active relay nodes through the traffic density in mobile sensor ad-hoc networks. We consider a setting in which a set of mobile sensor sources is creating data and a set of mobile sensor destinations receiving that data. We make the assumption that the network is massively dense, i.e., there are so many sources, destinations, and relay nodes, that it is best to describe the network in terms of macroscopic parameters, such as their spatial density, rather than in terms of microscopic parameters, such as their individual placements. We focus on a particular physical layer model that is characterized by the following assumptions: i) the nodes must only transport the data from the sources to the destinations, and do not need to sense the data at the sources, or deliver them at the destinations once the data arrive at their physical locations, and ii) the nodes have limited bandwidth available to them, but they use it optimally to locally achieve the network capacity. In this setting, the optimal distribution of nodes induces a traffic density that resembles the electric displacement that will be created if we substitute the sources and destinations with positive and negative charges respectively. The analogy between the two settings is very tight and have a direct interpretation in wireless sensor networks