## 5 The ATLAS MAC Protocol
### Traffic Load-Status Differentiation  
The radio capacity and traffic load are very closely coupled in a single shared wireless medium. Several issues such as contention, collision, internal and external interferences, hidden terminal problems and so forth have a direct impact on the network's traffic load. In ATLAS, we have estimated multi-level traffic load-status by taking the radio capacity usage into account. We are interested in estimating the traffic load at the cluster-head because, in the given network model (in Section 4.1), the cluster-head is more broadly representative of the dynamic network conditions and is a good indicator of any changes in the network. We have included a simplified traffic load estimation technique in which each cluster-head measures the load based on its capacity usage. Furthermore, we classify its traffic load status into the following categories: _low-load_, _moderate-load_, _high-load_, and _over-load_. Due to the variations in data traffic rates, each cluster-head needs to measure the load over an appropriate time interval. To accomplish this we use the same superframe/cycle duration \(T_{c}\) as the measurement period, and the _load index_, \(L_{i}\), of cluster-head \(Ch_{i}\) is given by,  
\[L_{i}=\frac{T_{c}\times(ar_{i}^{s}+fr_{i}^{s}+cr_{i})}{c\times\eta\times T_{c }}=\frac{ar_{i}^{s}+fr_{i}^{s}+cr_{i}}{c\times\eta} \tag{1}\]  
where \(c\) is the radio capacity and we use \(\eta\) as the maximum anticipated utilization of the capacity \(c\) due to the dynamic wireless environment in WBAN. Intuitively, \(\eta\) deduces the nominal capacity [24] of each cluster-head. Using the random network capacity analysis in [25] and our simulation parameters, the nominal capacity of each node is derived as a pre-task, where we set \(\eta=0.47\) (see Section 5.3). Moreover, \(ar_{i}^{s}\) and \(fr_{i}^{s}\) represent the successful packet's arrival and forwarding rates, respectively, and \(cr_{i}\) is the collision rate at \(Ch_{i}\). Using Equation (1) we calculate the total amount of traffic (_i.e._, arrived, forwarded and collided) at \(Ch_{i}\) during the \(T_{c}\) period. Note that a cluster-head considers only the successfully received and transmitted packets per unit time (_i.e._, seconds) to calculate the \(ar_{i}^{s}\) and \(fr_{i}^{s}\), respectively. In contrast, the \(cr_{i}\) expresses the number of collisions detected by \(Ch_{i}\) (either in sending or receiving) per-unit time; hence, the interference effects at \(Ch_{i}\) due to neighbors, interferers and hidden terminals are reflected by the measured \(cr_{i}\). The load estimation does not consider the losses due to the _bit error rate_ (BER) since the BER probability always remains same in an identical wireless environment. The categorization of the load-status based on the \(L_{i}\) is a critical task because the choice of load transition values requires a trade-off between capacity utilization and congestion/load awareness (_i.e._, the queue length of a node). A small value of \(L_{i}\) keeps the network in a low-congestion and contention state, in which the capacity utilization would be at a minimum. Conversely, although a large transition value can ensure better use of the capacity, it may quickly overload the network, resulting in more congestion (_i.e._, buffer overflow) and collision losses. Therefore, we have chosen suitable load transition values such that optimal capacity utilization and congestion avoidance can be guaranteed. Assume that the queue length of a cluster-head \(Ch_{i}\) is \(q_{i}\) and can hold 40 packets. We define lower and upper thresholds for \(q_{i}\), given by \(q_{i}^{l}\) and \(q_{i}^{u}\), respectively. In ATLAS, the values for \(q_{i}^{l}\) and \(q_{i}^{u}\) are set to be 3 and 8, respectively; and the analysis in [26, 27] validates these assumptions. In our extensive simulations, we have determined that when \(L_{i}\) exceeds 0.92, the queue length of a cluster-head always remains beyond \(q_{i}^{u}\).