We consider a wireless ad hoc network composed of N transmitters and M receivers which are all selfish in the sense that they wish to optimize their individual utilities rather than a network wide utility. Each node can acquire energy from a supplier at a price to power the transmission or reception of data. For such a network, we consider a matching game played between the transmitters and the receivers. The transmitters compute the optimal rate for them and propose this to a receiver. The receivers determine the best proposal they have received to maximize their utilities. We identify the optimal decisions for all nodes and the resulting utilities. We next consider a Vickrey auction between transmitters which have proposed to the same receiver. We show that the transmitters can compete with each other by offering energy transfer to the receiver. The energy transfer reduces the processing costs of the receiver and influences its decision, thereby pointing to the merit of energy cooperation. We observe that populating the network with additional nodes generally results in more options for all nodes to choose from, and larger rates for the entire network, which are improved even further by energy cooperation.

This paper studies energy harvesting transmitters in the single user channel, the two-way channel, and the two-way relay channel with block fading. Each transmitter is equipped with a finite battery to store the harvested energy, and a finite buffer to store the data that arrive during the communication session. We consider delay sensitive applications and maximize throughput while enabling timely delivery of data with delay constraints. We show that the resulting delay limited throughput maximization problem can be solved using alternating maximization of two decoupled problems termed the energy scheduling problem and the data scheduling problem. We solve the energy scheduling problem using a modified directional waterfilling algorithm with right permeable taps, water pumps, and overflow bins and the data scheduling problem with forward induction. Additionally, we identify the online optimum policy for throughput maximization. We provide numerical results to verify our analytical findings and to demonstrate the impact of the finite data buffer capacity and the delay requirements on the throughput. We observe that larger buffer sizes become useful for more lenient delay requirements, and a data buffer size that is comparable to the throughput within one time slot accounts for the majority of the increase in throughput.

In this paper, we study a cooperative two-hop network with multiple sources and multiple relays where the energy required for the relays is transferred by the sources. In return, the relays transmit the sources' data, along with their own data, to the destination. We consider the setup where each node's objective is to maximize the amount of its own data delivered to the destination. We take a game theoretic approach and first model the selfish cooperation scenario with one source and one relay as a Stackelberg game where (i) the relay or (ii) the source is the leader. We demonstrate how the leader of the game takes advantage of its ability to compute the follower's optimal strategy to influence the follower and improve its own utility. In both cases, we also consider the case with multiple followers. We employ Vickrey auctions to model the inter-follower competition. We identify the winner of the auction in both cases and observe that the followers must compromise their individual utilities to win the auction. Consequently, the leader's utility turns out to be nondecreasing in the number of competing followers.

We consider a two-hop wireless network where the source(s) in the network have the ability to wirelessly power the relay(s) who also have their own data to send to the destination. Considering the fact that each node in the network aims to maximize its own metric, we adopt a game theoretic approach that foresees offering relaying of the sources' data in exchange for energy provided to the relays, and simultaneously offering energy to the relays in exchange for their relaying services. We first study a Stackelberg competition with the single relay node as the leader, and investigate the impact of having multiple source nodes in the system. We next study the reciprocal Stackelberg game with the single source as the leader, and investigate the inter-relay competition with multiple relays. We find that in the Stackelberg games, the leader can improve its individual utility by influencing the follower's decision accordingly, even more so when there are multiple followers. We next formulate a noncooperative game between the source and the relay and show the existence of a unique Nash equilibrium by an appropriate pricing mechanism. The equilibrium maximizes the total utility of the network and allows the destination to choose how much data to receive from each node.

In this paper, we study the two-way relay channel with energy harvesting nodes. In particular, we find transmission policies that maximize the sum-throughput for two-way relay channels when the relay does not employ a data buffer. The relay can perform decode-and-forward, compress-and-forward, compute-and-forward, or amplify-and-forward relaying. Furthermore, we consider throughput improvement by dynamically choosing relaying strategies, resulting in hybrid relaying strategies. We show that an iterative generalized directional water-filling algorithm solves the offline throughput maximization problem, with the achievable sum-rate from an individual or hybrid relaying scheme. In addition to the optimum offline policy, we obtain the optimum online policy via dynamic programming. We provide numerical results for each relaying scheme to support the analytic findings, pointing out to the advantage of adapting the instantaneous relaying strategy to the available harvested energy.

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Network applications can benefit from accompanying these structural differences in propagation schemes. In this paper, we study the optimal influence propagation policies for networks with positive and negative relationship types. We tackle the problem of minimizing the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as frequency of interaction, the strength of friendship and foe ties, propagation delay or the impact factor of the propagating idea. We extend this problem by incorporating the impact of message deterioration and ignorance. We demonstrate our results in both a controlled environment and the Epinions dataset. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of influence propagation compared to naïve myopic algorithms.

In this paper, a single user channel is considered with an energy harvesting transmitter that receives its energy and data intermittently. The transmitter is equipped with a finite battery as well as a finite data buffer. The throughput maximization problem with a deadline is solved and the optimal transmission policy is obtained. The optimization problem is shown to yield a directional waterfilling solution with energy pumps. An alternative algorithmic solution is also presented that utilizes the recursive shortest path solution that was shown to be optimal for infinite data buffers in earlier work. Numerical results are provided to demonstrate the throughput performance of optimal policies as well as to assess the impact of the finite buffer on the throughput.