Message in delay/disruption tolerant network (DTN) is often transmitted in the store/carry/forward mode. In this mode, the transmission process includes two important steps (beaconing and forwarding). Both processes consume much energy which is very precious for the wireless application. At present, most of the works considers just one step in the controlling process (some ignore the beaconing control and some ignore the forwarding control). In addition, state-of-the-art works often assume that there is only one destination for a specific message. In this paper, we consider the optimal forwarding and beaconing control problem at the same time in DTN, where multiple destinations exist. In particular, based on whether those destinations are willing to relay message for others, we consider the selfish and altruistic cases, respectively. Based on these settings, we mathematically characterize this problem as an optimal control problem. The controlling parameters are the forwarding rate and beaconing rate, respectively. Then, we solve the problem by Pontryagin's Maximum Principle, and mathematically prove that both the optimal forwarding and beaconing policies conform to the threshold form. Next, we check the accuracy of the theoretical models by simulations based on both synthetic and real motion traces. Finally, we check the result in a real scenario with 100 students, and show that the optimal policy can really improve the performance.

Participatory sensing is a crowdsourcing-based framework, where the platform executes the sensing requests with the help of many common peoples handheld devices (typically smartphones). In this paper, we mainly address the online sensing request admission and smartphone selection problem to maximize the profit of the platform, taking into account the queue backlog, and the location of sensing requests and smartphones. First, we formulate this problem as a discrete time model and design a location aware online admission and selection control algorithm (LAAS) based on the Lyapunov optimization technique. The LAAS algorithm only depends on the currently available information and makes all the control decisions independently and simultaneously. Next, we utilize the recent advancement of the accurate prediction of smartphones mobility and sensing request arrival information in the next few time slots and develop a predictive location aware admission and selection control algorithm (PLAAS). We further design a greedy predictive location aware admission and selection control algorithm (GPLAAS) to achieve the online implementation of PLAAS approximately and iteratively. Theoretical analysis shows that under any control parameter V > 0, both LAAS and PLAAS algorithm can achieve O(1/V)-optimal average profit, while the sensing request backlog is bounded by O(V). Extensive numerical results based on both synthetic and real trace show that LAAS outperforms the Greedy algorithm and Random algorithm and GPLAAS improves the profit-backlog tradeoff over LAAS.

In mobile crowdsensing (MCS), the battery of participants is often limited. When participants perform too many sensing tasks resulting in insufficient remaining energy, they will exit the MCS system. This article mainly addresses the energy balancing problem to prolong the system lifespan. By this means, it can ensure adequate participants and promote the completion of tasks. First, it formulates a discrete time optimization model, which transforms abovementioned problem into the online control of task admission and allocation. In addition, this model uses remaining energy variance of the participants to measure the degree of balance. Next, an online energy balancing strategy (OEBS) is proposed based on the Lyapunov optimization, which can realize energy balance without utility loss. Finally, an approximate optimal policy is presented based on the linear programming and genetic algorithm to solve abovementioned optimization problem. Experiments show that OEBS effectively maintains adequate participants, prolongs the lifespan of MCS system and maximizes the system utility even when there are few participants with multiple tasks. Specifically, the lifespan in OEBS is longer than that in utility optimization algorithm (UOA) and LP-relaxation algorithm significantly. The total utility in OEBS is more than that in UOA. OEBS can maximizes average utility of system by adjusting ${\bm{V}}$ . In addition, the energy balancing ability of OEBS is always effective as ${\bm{V}}$ changes.