Unlike the first class protocols that utilize local information, they can spread the traffic to perform the load balance since they use the cumulative path load.
In particular, existing protocols allow a sensor node to construct its gradient using the cumulative traffic load of a path for load balancing.
In the second class, each sensor node constructs its gradient using the cumulative traffic load information a of the path potentially used for its data delivery.
Figure 3 presents our data using the cumulative distribution of guesses and donations in each condition.
In GLOBAL, in order to allow a sensor node to use the least-loaded path which also avoids the most overloaded sensor node, each sensor node calculates its gradient using the weighted average (WA) of the cumulative path load and traffic load of the most overloaded node over the path.
On the other hand, since CPL creates it gradient field, based on only the cumulative path load, it cannot efficiently avoid using paths containing the most overloaded node.
However, with the gradient model driven by Equation 3, a sensor node cannot efficiently avoid using the path with the most overloaded node among its all possible paths since only the cumulative path load is not enough to identify the path including the most overloaded node [13].
However, they have some drawbacks that a sensor node cannot efficiently avoid using the path with the most overloaded node among its all possible paths since only the cumulative path load is not enough to identify the path including the most overloaded node [13].
It hints at the cumulative, path dependent nature of learning processes.
The cumulative path cost is defined as the sum of link costs along the path.
