Since the seminal work of Grossglauser and Tse [1], the two-hop relay
algorithm and its variants have been attractive for mobile ad hoc networks
(MANETs) due to their simplicity and efficiency. However, most literature
assumed an infinite buffer size for each node, which is obviously not
applicable to a realistic MANET. In this paper, we focus on the exact
throughput capacity study of two-hop relay MANETs under the practical finite
relay buffer scenario. The arrival process and departure process of the relay
queue are fully characterized, and an ergodic Markov chain-based framework is
also provided. With this framework, we obtain the limiting distribution of the
relay queue and derive the throughput capacity under any relay buffer size.
Extensive simulation results are provided to validate our theoretical framework
and explore the relationship among the throughput capacity, the relay buffer
size and the number of nodes
To meet some real-time mobile crowd sensing (MCS) scenarios, there is a tendency to enhance the MCS system with mobile edge computing (MEC). One of the key challenges is how to select some satisfied participants in such an edge-cloud collaboration MCS system to effectively and real-timely handle dynamic and heterogeneous sensing tasks. In this paper, we propose a bilateral satisfaction aware participant selection mechanism in the edge-cloud collaboration MCS system. The participant selection process is coordinated by the cloud service platform and the MEC server. The cloud service platform sends the required data types to the MEC server and evaluates the user reputation through the user history task records. The MEC server generates a set of tasks and evaluates user fitness based on the user's real-time location. Then the MEC server obtains the user sensing cost based on the user status, and develops the task price model based on the user supply index and data demand index. Finally, the participant selection process is transformed into a game between users and the MEC server about the task reward, and the user who accepts the optimal task price is selected as the participant. The results show that the proposed participant selection strategy can effectively reduce the amount of data processed by the cloud platform, shorten the task completion time, and increase bilateral satisfaction. INDEX TERMS Crowd sensing, edge computing, participant selection, user characteristic, bilateral satisfaction.
Recently, researchers showed that "dirty paper coding" (DPC) achieves the capacity region of MIMO Gaussian broadcast channels (MIMO-BC). So far, there has been little study on how this fundamental information-theoretic result will impact the cross-layer design for MIMO-based ad hoc networks. To fill this gap, we consider the problem of jointly optimizing DPC power allocation at the physical layer and multihop/multipath routing at the network layer for MIMObased ad hoc networks. This optimization problem turns out to be a challenging non-convex problem. To address this difficulty, we transform the original problem to an equivalent problem by exploiting the uplink-downlink duality. For the transformed problem, we propose a solution procedure that integrates Lagrangian dual decomposition, conjugate gradient projection based on matrix differential calculus, and cutting-plane methods.
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