Semi‐myopic algorithm for resource allocation in wireless body area networks

Abstract

The key features of wireless body area networks are limited energy resources of the sensor nodes and the need for highly reliable packet transmission. Therefore, designing a suitable algorithm that schedules transmission of the nodes is very important. Although an optimal algorithm has been previously reported based on a partially observable Markov decision process (POMDP), its complexity is high. In this study, the authors propose a suboptimal algorithm for scheduling the transmissions, i.e. a semi-myopic algorithm, with a performance close to the optimal, albeit with much lower complexity. To this end, they modify the structure of the cost function of the optimal algorithm such that the scheduling decision at each superframe is made regarding its effect on only a few upcoming superframes. The authors' proposed algorithm is considered in two different incoming packet scenarios, deterministic and random arrivals. Simulation results show that for both types of arrivals, there is a negligible difference between their proposed algorithm and the optimal one, i.e. POMDP, in terms of energy consumption and reliability.

Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

“…Karimzadeh-Farshbafan and Ashtiani proposed a suboptimal algorithm for scheduling transmissions, the semimyopia algorithm, which is much less complex than previous algorithms, but has near-optimal performance. Simulation results show that the difference in energy between his proposed algorithm and the optimal algorithm (i.e., POMDP) is negligible [8]. Although these theories discuss M2M technology and wireless resource allocation to a certain extent, the combination of the two is less discussed and not practical…”

Optimal clustering algorithm for wireless sensor networks using combined turbid ant swarm and improved myopic algorithm for maximizing life of sensors

“…The myopic optimization models [33] are used for imperfect forethought and short-term focus of speculation decision makers, leading to further realistic outcome. The view window considers accurate information for certain years, and information beyond this period is not available.…”

“…The problem of aggravating myopia is difficult, but it has optimal access to the oracle. The advanced myopic algorithm is inspired by the traditional method [33], the main purpose of which is to identify random reduction problems, for which myopic heuristics ensure a consistent optimization factor. We use an improved myopic approach to the CH sample process with multiple controls such as power consumption, grid lifetime, routing cost, grid load and distance.…”