Dynamic resource allocation for opportunistic software-defined IoT networks: stochastic optimization framework

Several wireless technologies have recently emerged to enable efficient and scalable internet-of-things (IoT) networking. Cognitive radio (CR) technology, enabled by software-defined radios, is considered one of the main IoT-enabling technologies that can provide opportunistic wireless access to a large number of connected IoT devices. An important challenge in this domain is how to dynamically enable IoT transmissions while achieving efficient spectrum usage with a minimum total power consumption under interference and traffic demand uncertainty. Toward this end, we propose a dynamic bandwidth/channel/power allocation algorithm that aims at maximizing the overall network’s throughput while selecting the set of power resulting in the minimum total transmission power. This problem can be formulated as a two-stage binary linear stochastic programming. Because the interference over different channels is a continuous random variable and noting that the interference statistics are highly correlated, a suboptimal sampling solution is proposed. Our proposed algorithm is an adaptive algorithm that is to be periodically conducted over time to consider the changes of the channel and interference conditions. Numerical results indicate that our proposed algorithm significantly increases the number of simultaneous IoT transmissions compared to a typical algorithm, and hence, the achieved throughput is improved

A Formula for Successful Transmission Probability in Opportunistic Networks Under Memory-Time Correlated Channel Availability

Dynamic Spectrum Access (DSA) technology in wireless Cognitive Radio Networks (CRNs) provides opportunistic access for unlicensed users, also known as Secondary Users (SUs), which can offer huge bandwidth to enable future wireless communication. Mainly, this technology aims to improve the end-to-end throughput by allowing SUs to exploit the licensed channels only when their licensed users, also known as Primary Users (PUs), are not using them. Most existing communication protocols designed for CRNs are based on the assumption that the channel availability time is considered based on a memory-less distribution for PUs arrivals. Unfortunately, this assumption is impractical because the PU channels’ activity and availability are memory-time correlated. Worse yet, designing communication protocols for CRNs under this assumption can result in overestimating the Probability of Success (PoS) for SU packet transmissions, resulting in severe degradation in network performance in realistic scenarios. This paper derives a closed-form formula under memory-time correlation for channel availability that quantifies the PoS for SUs’ packet transmission in CRNs. This will empower the network designers to get practical expectations about network efficiency rather than the overestimated PoS. Therefore, this work is also useful for emerging wireless networks with multi-hop routing, such as 5G, 6G, vehicular networks, etc., which incorporate DSA techniques. Our numerical and simulation results demonstrate that the PoS is overestimated in most of the literature due to adopting memoryless-based distribution in modeling channels’ availability; such overestimation can impact communication protocol decisions, resulting in severe network performance degradation