Distributed Optimization for Energy-Efficient Internet-of-Things Systems with Dynamic Resource Constraints
Keywords:
Age of Information; Internet of Things; stochastic optimization; distributed optimization; opportunistic routing; energy efficiency; CVaR.Abstract
Maintaining information freshness and conserving energy are crucial challenges in dynamic Internet of Things (IoT) networks. The work proposed in this paper presents a modification of an energy-balanced opportunistic routing framework by incorporating the Age of Information (AoI) into a stochastic optimization model. The optimization model aims to balance factors such as AoI, AoI imbalance, energy consumption, network throughput, and the tail risk of AoI. To control excessive stale information, the optimization model incorporates Conditional Value-at-Risk (CVaR) into its calculations. The paper also presents a distributed stochastic primal-dual algorithm that aims to adapt the routing and transmission decisions of nodes in the IoT network based on the channel conditions, the success of packet transmissions, and the dynamic connectivity of the network. The paper proves that this algorithm can preserve the feasibility of the optimization problem and can converge to a stationary solution of the relaxed optimization problem under appropriate conditions. Numerical simulations performed in the paper verify the trade-offs between network freshness, energy consumption, throughput, and network lifetime in IoT networks, as well as the benefits of using AoI imbalance and CVaR in optimization problems of this kind.
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