Optimal and quasi-optimal energy-efficient storage sharing for opportunistic sensor networks

This paper investigates optimum distributed storage techniques for data preservation, and eventual dissemination, in opportunistic heterogeneous wireless sensor networks where data collection is intermittent and exhibits spatio-temporal randomness. The proposed techniques involve optimally sharing the sensor nodes' storage and properly handling the storage traffic such that the buffering capacity of the network approaches its total storage capacity with minimum energy. The paper develops an integer linear programming (ILP) model, analyses the emergence of storage traffic in the network, provides performance bounds, assesses performance sensitivities and develops quasi-optimal decentralized heuristics that can reasonably handle the problem in a practical implementation. These include the Closest Availability (CA) and Storage Gradient (SG) heuristics whose performance is shown to be within only 10% and 6% of the dynamic optimum allocation, respectively

Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage

In this paper, we consider delay minimization for interference networks with renewable energy source, where the transmission power of a node comes from both the conventional utility power (AC power) and the renewable energy source. We assume the transmission power of each node is a function of the local channel state, local data queue state and local energy queue state only. In turn, we consider two delay optimization formulations, namely the decentralized partially observable Markov decision process (DEC-POMDP) and Non-cooperative partially observable stochastic game (POSG). In DEC-POMDP formulation, we derive a decentralized online learning algorithm to determine the control actions and Lagrangian multipliers (LMs) simultaneously, based on the policy gradient approach. Under some mild technical conditions, the proposed decentralized policy gradient algorithm converges almost surely to a local optimal solution. On the other hand, in the non-cooperative POSG formulation, the transmitter nodes are non-cooperative. We extend the decentralized policy gradient solution and establish the technical proof for almost-sure convergence of the learning algorithms. In both cases, the solutions are very robust to model variations. Finally, the delay performance of the proposed solutions are compared with conventional baseline schemes for interference networks and it is illustrated that substantial delay performance gain and energy savings can be achieved