## 2 Related Work

Table 2: _Cont._incorporates time-slotted access with multiple channel and channel hopping. It is popular mainly due to its interoperability with the Internet of Things (IoTs) with appropriate support to mesh networks. As TSCH more suitable for high coverage scenarios particularly node-coordinator scenarios like patient monitoring systems are ignored. The DSME is proposed for applications which require time bounded services with high reliability, especially for industrial and healthcare applications. It is the improved form of IEEE 802.15.4 which provides the adoptability option for time-varying traffic which is lacking in the original standard. DSME improves the performance of 802.15.4 by increasing the number of GTS slots and operating frequency channels. Moreover, it introduces the concept of the multi-superframe structure which is capable of handling both periodic and aperiodic traffic. The best channel is selected to provide the high reliability. In multi-superframe, various CFP and CAP periods are aggregated. However, DSME lacks implementation examples due to its complex mechanisms. Further the nodes are required to stay in the active mode which increases the energy utilization. LLDN is a TDMA based mechanism and depends on a simple superframe structure where each node can get the exclusive access for a timeslot [39]. However, LLDN is not suitable for time-varying traffic; moreover, it requires information regarding number of nodes and packet size. In [35], a decentralized approach DT-SCS an improved version of TSCH is presented by using the pulsed-couple oscillators that simultaneously do the synchronization and desynchronization over multiple channels. This approach is more suitable for vehicular environment, whereas in WBASNs a centralized approach with a controller/coordinator is more successful; moreover, channel switching mechanism is still a source of delay which is a challenge for medical applications. The reinforcement learning based approach [36] is presented for a distributed environment; this approach provides energy efficient communication while maintain the high throughput. However, the computational complexity is involved which is less likely for WBASNs in terms of short memory of sensors and the requirement of limited delay. In [40] the authors describe the basic requirements of a body sensor network (BSN)-based software framework for signal processing applications. Signal processing in a node environment (SPINE) is proposed as an efficient framework for signal processing applications for BSN-based networks. SPINE is tested on hardware and software platforms. In [41] a detailed survey of BSN is provided in the context of fusion of the data from multiple heterogeneous biomedical sensor devices located around the body. The paper specifically discusses the issues of physical activity recognition; moreover, it provides the comparison of different existing platforms based on distinctive properties that affect the design of data fusion. Due to the abovementioned limitations and complex solutions, this paper proposes using the frame aggregation mechanism with existing MAC of IEEE 802.15.4 to optimize the performance of the standard in terms of time bounded services, appropriate throughput, reliability, and energy efficiency.