## 2 Overview of Candidate Technologies for Wearable Wireless Networks
### Overview of Key WWN Applications and Implementations  
The wireless technologies given above are considered as current and prospective technologies which fulfill the BAN and BBN communications. Wireless technology is selected and implemented depending on the requirements of the applications and use cases. [PERSON] et al. in [13] classify the applications into three main classes: (1) remote health and fitness monitoring; (2) military and training; and (3) intelligent biosensors for vehicle-area-networks. Moreover, authors in [13] discuss a list of research projects and implementations, in particular: Advanced Health and Disaster Aid Network (AID-N) [14] targets disaster and public safety applications. AID-N uses wired connection for BAN communication, mesh and ZigBee for BBN. Off-body communication in AID-N are fulfilled through WiFi, cellular networks and the Internet. AID-N aims to sense pulse, blood pressure, temperature and Electrocardiography (ECG). [PERSON] et al. in [15] focus more on the main medical applications: (1) telemedicine and remote patient monitoring; (2) rehabilitation and therapy; (3) biofeedback; and (4) ambient assisted living. The latter work discusses also the Quality of Service (QoS) requirements for the medical context. The earliest proposed schemes target to enhance the on-body devices transmission reliability and improve energy efficiency. [PERSON] et al. in [16] proposed a novel cross-layer design optimization (CLDO) scheme. Indeed, the design of CLDO relies on the three lower layers (i.e., PHY, MAC and network layer). Power consumption is firstly optimized by selecting optimal power relays. Then, the remaining energy in leaf nodes is utilized to increase the lifetime and the reliability. An optimal packet size is given for energy efficiency. [PERSON] et al. claim that an inevitably slight overheadaccompanies CLDO processing for different factors. First, during network initialization complex procedures are run. Second, the algorithm uses a certain number of iterations which influences the overall performance. Third, CLDO lacks the capacity to manage dynamic location situations. Recent optimization of existing approaches has been proposed to draw a mathematical model for joint single path routing and relay deployment in BAN design. An interesting algorithm has been introduced by [PERSON] et al. in [PERSON] et al. (2017) to handle the uncertainty that affects traffic demands in the multiperiod capacitated network design problem (MP-CNDP). Additionally, a hybrid primal heuristic based on the combination of a randomized fixing algorithm was proposed by the authors, inspired from ant colony optimization and exact large neighborhood search. Performance of the proposed model has been confirmed based on computational experiment compared to existing solutions. This strategy has been improved by [PERSON] et al. in [PERSON] et al. (2017). The authors adopt a best performance solution based on a min-max approach [PERSON] et al. (2017). Indeed, the proposed algorithm relies on a combination of a probabilistic fixing procedure, guided by linear relaxations, and an exact large variable neighborhood search [PERSON] et al. (2017). This combination has been inspired by the solution methods approximate nondeterministic tree-search (ANTS) [PERSON] et al. (2017), ant colony optimization [PERSON] et al. (2017) and other randomized algorithms [PERSON] et al. (2017). [PERSON] et al. extended their preliminary work [PERSON] et al. (2017) by new integer linear programming (ILP) heuristic to solve the design problem. The new techniques detailed in [PERSON] et al. (2017) do not only fix the variables expressing routing decisions, but also employ an initial deterministic fixing phase of the variables modeling the activation of relay nodes. Experiment conducted by this work shows that the proposed approach outperforms the existing optimization solvers strategies and the results recorded in [PERSON] et al. (2017). In [PERSON] et al.