In a network context, a sink is a device or destination that receives data from other devices but does not forward or route the data to other devices. A sink typically represents the endpoint of a data flow within a network topology. It is commonly used in scenarios like data collection, monitoring, or analytics where the focus is on receiving and processing incoming data rather than transmitting it further.
Rocks sink because they are denser than water, causing them to displace less water and therefore sink. Metal objects sink due to their high density, which makes them heavier than water and causes them to sink. Sunken ships sink because they take on water, increasing their overall weight and causing them to sink below the water's surface.
Anorthite has a specific gravity greater than 3, which means it is denser than water and will sink in it.
Marbles are denser than water, so they will sink when placed in water.
Probably because it draws/absorbs the heat making the heat "sink" into it
When you sink in the pool, it is because your body is denser than the water. This causes you to displace water and sink below the surface.
Sink Tree is a description in a routing table of all the paths in a network to a destination.
Sink mobility refers to the ability of sink nodes in a wireless sensor network to move from one location to another. This movement can be intentional or due to external factors. Sink mobility can help improve network coverage, balance energy consumption, and optimize data collection in dynamic environments.
An air gap is a network security measure consisting of ensuring a secure computer network is isolated from unsecured networks, such as the public Internet or an unsecured local area network.
Directed diffusion is a query-based protocol where a query is flooded in the network by the sink where multiple routes are established between the sink and source. The sink reinforces one of the paths and receives data in a shorter interval through this reinforced path.
The Ford-Fulkerson algorithm is used to find the maximum flow in a network, which is the maximum amount of flow that can be sent from a source node to a sink node in a network.
Sensor network comprises of scattered sensor nodes with limited computational capabilities and battery power. The existing security solutions for traditional wireless networks can not be used because of the constraints associated with sensor network. We present secure sink node architecture as two-tiered scheme for sensor network security. The architecture protects the sink node from unauthorized access by surrounding it with two protection layers. Sink nodes listen to only inner layer nodes and inner nodes are allowed to communicate with only outer layer nodes. These protection layers are formed in an intelligent manner without violating constraints specific to sensor network. In order to enhance security, protection layers are re-adjusted in case of an attack. We present statistical analysis to elucidate the performance of proposed architecture.
The solution to the maximum flow problem is finding the maximum amount of flow that can be sent from a source to a sink in a network. This helps optimize the flow of resources by determining the most efficient way to allocate resources and minimize bottlenecks in the network.
The future tense of "sink" is "will sink".
In a maximum flow problem, the goal is to determine the maximum amount of flow that can be sent from a source node to a sink node in a network. One example of a solved maximum flow problem is the Ford-Fulkerson algorithm applied to a transportation network where the source node represents a factory and the sink node represents a warehouse. The algorithm calculates the maximum amount of goods that can be transported from the factory to the warehouse through various paths in the network, taking into account the capacities of the edges connecting the nodes.
The maximum flow problem is a mathematical optimization problem that involves finding the maximum amount of flow that can be sent through a network from a source to a sink. It is used in network optimization to determine the most efficient way to route resources or information through a network, such as in transportation systems or communication networks. By solving the maximum flow problem, optimal routes can be identified to minimize congestion and maximize efficiency in the network.
In a network with lower bounds on the flow of each edge, the maximum flow that can be achieved is the total flow that satisfies all the lower bounds on the edges while maximizing the flow from the source to the sink.
As in 'sink a boat' or 'the kitchen sink'?