# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 2 Related Work
### Wireless Sensor Networks: Metrics  
Accounting for the relevance that WSNs currently exhibit, optimizations related to metrics have been an ongoing subject of study for researchers. Due to their battery-operated nature (and thus limited lifetime), extending the lifetime of the network is therefore a critical issue. Since current consumption on wireless nodes can be mostly attributed to either CPU operation or transceiver (network) usage, it is clear that in order to maximize lifetime, those power-hungry components must be used as little as possible. This fact is easily verified by the datasheets from popular wireless transceivers [20, 21]. However, there are a number of factors that can come into play when it is no longer possible to minimize either CPU idle or network sleep. As pointed out in [1], traffic load imbalances lead to faster battery depletions in the nodes that assume a higher network operation. This accelerated discharge is the factor that causes connectivity losses in the network as those nodes that have exhausted their batteries are now unable to route the traffic originating from their neighbors, thus forming gaps in the network with no connection. Regarding network longevity, [22] highlights the problems with the different WSN longevity strategies in relation to their actual usefulness and presents some useful terms. _First node dies_, or the time until the first node runs out of power, may result too conservative because it assumes that the network dies when the first node runs out of power. _Last node dies_, on the other hand, measures the time until all nodes run out of power. Maximizing _last node dies_ can be a misleading measure because it depends on the idle time of the least active nodes. Alternative metrics include the so-called beta surviving nodes, which represent a minimum proportion of active nodes required for the network to be considered operational. However, determining this value presents a significant challenge. In addition to the aforementioned strategies, there are those based on coverage. 'k-coverage' measures the time during which each region is covered by at least k sensors. 'Alpha-coverage' quantifies the time during which the coverage ratio of a region is greater than an alpha threshold. It should be noted that it is necessary to not only cover a region, but also to be able to transmit these data (_connected coverage_). The authors' aimed contribution is to minimize redundant traffic and therefore consumption by assigning different roles in the network. In order to enhance deployment, the contribution presented in [23] employs an evolutionary algorithm to improve the layout of the nodes in WSNs. In practice, the issue can be framed as an optimization problem, with the objective of minimizing energy consumption at the node with the highest consumption. This approach is entirely simulation-based, which presents a challenge in terms of its applicability to actual nodes. Similarly, ref. [24] focuses on reducing energy dissipation by proposing a relay-based clustering approach, thus reducing multi-hop communication. A simulation model of the LEACH routing protocol is used. Other trends in the literature tend to consider network lifetime as a secondary aspect, emphasising instead the importance of network connectivity. In [25], authors present a mathematical model for WSN, which they entitle 'Probabilistic Network Connectivity Algorithm (PNCA)', considering the distribution of nodes as a Poisson process and the connectivity between them as a binomial process. MATLAB R2018a is used to simulate their proposed optimization algorithm for a number of 50 nodes. The problem to be solved is based on the search for the optimal deployment to maximize connectivity. This connectivity is a probability (network probability) as a function of the connectivity factor with a fixed communication radius and, conversely, the network probability for a fixed connectivity factor value equal to two and different coverage radius values.