Optimal Packet Aggregation Scheduling in Wireless Networks

One of the most critical emerging problems for 5G and Internet of Things is the handling of machine-to-machine communication. Wireless sensor networks are deployed every day, resulting in a more distributed infrastructure, where the communication and processing are handled by energy, bandwidth, and processing constrained devices. Aggregation of multiple packets flowing over the same path increases spectral efficiency, energy efficiency, and resource utilization. We address the problem of determining the optimal waiting time to maximize the utility within the network. We provide a general framework, where the utility function is user-defined for each individual application stream and packet. This allows the user to optimize for energy, delay, or expiration rate in the resolution of individual streams. Our algorithm calculates the optimal time for any given condition on-the-fly and can adapt to changing conditions with low computational complexity. We provide an optimal multi-hop distributed and scalable under congestion versions of our algorithm. Our simulations in ns3 show that we outperform state-of-the-art policies by 1.55x in terms of information freshness. Our solution reduces average power consumption by more than 60 percent. Our congestion-aware solution shows constant performance with increasing congestion levels, whereas state-of-the-art solutions degrade by up to 70 percent under the same conditions.