Sentence examples for maximum network coverage from inspiring English sources

In [22, 23], the sensors are moved to optimal positions so that maximum network coverage is achieved.

When the maximum network coverage rate is selected as the optimal objective, network sensor nodes with hexagon structure distributed in the monitoring area, the minimum working nodes set can be obtained [33].

The aims of wireless sensor network node optimal coverage are to reduce the sensor node's utilization and to ensure maximum network coverage at the same time, this is a multi-objective optimization problem.

The wireless sensor network node optimal coverage is a typical multi-objective optimization problem, this problem can be expressed as follows: existing sensor set S = (s 1, s 2, ⋯, s i, ⋯, s N  ), seeking the working set S′, which can obtain the maximum network coverage rate, and the minimum working node set.

From formula (5), we know that the maximum network coverage f 1(S′) and minimum working nodes set f 2(S′) are contradictory; compromising the two, the multi-objective optimization coverage model of wireless sensor network is changed into the maximum objective function f(S′).

The network coverage needs to reach the maximum under the conditions that guarantee a certain quality of service (QoS) [18 21], by measuring the network coverage to determine whether the presence of communication blind area and get the monitoring area of wireless sensor network coverage.

Unlike in some probability-based approaches, where every node is assigned a fixed probability that does not ensure full network coverage, the technique proposed in this article combines concepts from maximum range node selection with node pruning to reduce redundant re-transmissions in route request but offer connectivity and better network coverage guarantees inherent in deterministic techniques.

In the next paper, C.-H. Hsu et al. propose layered elimination optimization (LEO) which is an algorithm-independent technique aiming to detect maximum amount of redundant readers that could be safely removed or turned off with preserving original RFID network coverage.

In this paper, we present a layered elimination optimization (LEO) which is an algorithm-independent technique aims to detect maximum amount of redundant readers that could be safely removed or turned off with preserving original RFID network coverage.

The network coverage of the wireless network in meters plus 936 m (calculated using Eq. (1)) is the maximum distance one cloudlet should have with at least another cloudlet from a VPN.

Imagine network coverage of that!