# Self-Learning Power Control in Wireless Sensor Networks
## 1 Introduction  
Wireless Sensor Networks (WSNs) are dense mesh networks, thus techniques of multiple access to the medium are necessary to manage the communications among the nodes [1]. Such techniques suggest multiple transmissions from different sources, which should not interfere with one another. The transmissions can be scheduled in various dimensions. For instance, in the case of Time Division Multiple Access (TDMA), the time is divided in slots, which are assigned to each source for transmission. Only one stream of information can occur per slot, so multiple transmissions are serialized. In Frequency Division Multiple Access (FDMA), the traffic of the sources are parallelized in frequency per allocated channel. Simultaneous communications can happen, proportionally to the number of channels available. In Code Division Multiple Access (CDMA), transmitters can send packets at the same time, using orthogonal codes multiplied to the symbols that are transmitted/received. In this case, the number of simultaneous transmissions is limited to the number of orthogonal codes. In addition, two more techniques allow concurrent transmissions: Space Division Multiple Access (SDMA) and Carrier Sense Multiple Access (CSMA). These techniques are very sensitive to the dynamics of the network. In SDMA, the communications are confined in zones of the network to reduce contention, while maintaining the connectivity of the network. Using CSMA,the spectrum is scanned and analysed to discover the interested carrier. Both SDMA and CSMA are regulated by either a standard or a de facto protocol. Their advantage is that communications are flexible and decentralized, without the need of synchronization as in TDMA and FDMA. The drawback is that they do not ensure a network free of collision. Thus, the design of the regulations is important for the minimization of the collisions. Spatial reuse and contention mitigation in SDMA are obtained by dimming the transmission power of the nodes in a network. The decision of the transmission power is dependent on many factors: distance between transmitter and receiver, density of the network, energy available by the nodes, fading of the channel, mobility and performance constraints. In particular, two fields of protocols can be considered for transmission power decision, namely Topology Control (TC) and Transmission Power Control (TPC) [2]. The former acts at the network layer, thus the value of transmission power per node is chosen with respect to the routing path that the traffic can take to reach the destination. The path can either be point-to-point or multi-hop with variable number of hops. Unlike TC, TPC is link-based, used only for point-to-point communications by means of link quality monitoring. TPC is used in WSNs, whereby energy and spectrum efficiency are particularly critical. Energy consumption is a typical problem, given the constrained resources of the sensor nodes, including limited battery capacity [3]. The latter should guarantee a long period of lifetime, more than a decade, where the sensor nodes operate without human intervention [4]. For this reason, sleeping techniques for energy efficiency are proposed in the literature to carefully allocate operational time and reduce idle periods [5; 6]. WSNs were initially developed as independent, isolated entities for Machine to Machine (M2M) communications, providing simple operations, such as the monitoring of physical phenomena and executing actions. Therefore, the energy efficiency was prioritized by researchers, neglecting the Quality of Service (QoS). Instead, in the context of Internet of Things (IoT), WSNs are interconnected with other networks, providing services to users through Internet (e.g., smart meters [7]). Hence, QoS becomes more relevant, with regard to network reliability, to respect performance requirements of various applications [8].