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

Multi-objective hierarchical algorithms for restoring Wireless Sensor Network connectivity in known environments

A Wireless Sensor Network can become partitioned due to node failure, requiring the deployment of additional relay nodes in order to restore network connectivity. This introduces an optimisation problem involving a tradeoff between the number of additional nodes that are required and the costs of moving through the sensor field for the purpose of node placement. This tradeoff is application-dependent, influenced for example by the relative urgency of network restoration. We propose a family of algorithms based on hierarchical objectives including complete algorithms and heuristics which integrate network design with path planning, recognising the impact of obstacles on mobility and communication. We conduct an empirical evaluation of the algorithms on random connectivity and mobility graphs, showing their relative performance in terms of node and path costs, and assessing their execution speeds. Finally, we examine how the relative importance of the two objectives influences the choice of algorithm. In summary, the algorithms which prioritise the node cost tend to find graphs with fewer nodes, while the algorithm which prioritise the cost of moving find slightly larger solutions but with cheaper mobility costs. The heuristic algorithms are close to the optimal algorithms in node cost, and higher in mobility costs. For fast moving agents, the node algorithms are preferred for total restoration time, and for slow agents, the path algorithms are preferred

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