When experiments are performed on social networks, it is difficult to justify the usual assumption of treatment-unit additivity, due to the connections between actors in the network. We investigate how connections between experimental units affect the design of experiments on those experimental units. Specifically, where we have unstructured treatments, whose effects propagate according to a linear network effects model which we introduce, we show that optimal designs are no longer necessarily balanced; we further demonstrate how experiments which do not take a network effect into account can lead to much higher variance than necessary and/or a large bias. We show the use of this methodology in a very wide range of experiments in agricultural trials, and crossover trials, as well as experiments on connected individuals in a social network.
Abstract-The Internet of Things (IoT) technology with huge number power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device, however, this could also degrade the Quality of Service (QoS) provisioning. In this paper, we study the problem of how to achieve the tradeoff between the QoS provisioning and the energy efficiency for the industrial IoT systems. We first formulate the multi-objective optimization problem to achieve the objective of balancing the outage performance and the network lifetime. Then we propose to combine the Quantum Particle Swarm Optimization (QPSO) with the improved Non-dominated Sorting Genetic algorithm (NSGA-II) to obtain the Pareto optimal front. In particular, NSGA-II is applied to solve the formulated multi-objective optimization problem and QPSO algorithm is used to obtain the optimum cooperative coalition. The simulation results suggest that the proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance.
Restricted Access Window (RAW) has been introduced to IEEE 802.11ah MAC layer to decrease collision probability. However, the inappropriate application of RAW for different groups of devices would increase uplink energy consumption and decrease data rate. In this paper, we study an energy efficient RAW optimization problem for IEEE 802.11ah based uplink communications. We first present a novel retransmission scheme that utilizes the nextt empty slot to retransmit for collided devices, and formulate the problem based on overall energy consumption and the data rate of each RAW by applying probability theory and Markov Chain. Then, we derive the energy efficiency of the uplink transmission. Last but not the least, an energy-aware window control algorithm to adapt the RAW size is proposed to optimize the energy efficiency by identifying the number of slots in each RAW for different group scales. Simulation results show that our proposed algorithm outperforms existing RAW on uplink energy efficiency and delivery ratio.
Packet level measurement is now critical to many aspects of broadband networking, e.g. for guaranteeing Service Level Agreements, facilitating measurement-based admission control algorithms, and performing network tomography. Because it is often impossible to measure all the data passing across a network, the most widely used method of measurement works by injecting probe packets. The probes provide samples of the packet loss and delay, and from these samples the loss and delay performance of the traffic as a whole can be deduced. However measuring performance like this is prone to errors. Recent work has shown that some of these errors are minimized by using a gamma renewal process as the optimal pattern for the time instants at which to inject probes.This leaves the best rate at which to inject probes as the key unsolved problem, and we address this here by using the statistical principles of the Design of Experiments. The experimental design approach allows us to treat packet level measurements as numerical experiments that can be designed optimally. Modelling the overflow of buffers as a 2-state Markov chain, we deduce the system's likelihood function, and from this we develop a technique (using the Fisher information matrix) to determine the upper-bound on the optimal rate of probing. A generalization of this method accounts for the effect of the probed observations interfering with the experiment. Our numerical results focus on VoIP traffic, allowing us to show how this methodology would be used in practice. One application of this is in measurement-based admission control algorithms, where our technique can be used to provide an upper-bound on the rate at which probes should be injected to monitor the loss performance of the target network, prior to making an admit / don't admit decision.
Low-power machine-type communications devices in machine-to-machine networks are expected to operate autonomously for years, or even decades. Meanwhile, device-to-device (D2D) communications make large benefits on users' data rate and power consumption because of the proximity between potential D2D transmitters and receivers. In this paper, we facilitate machine-type D2D links where the machine-type communications devices are connected to a nearby device, such as a number of sensors connected to a gateway that acts as the relay towards the evolved NodeB. A challenging problem is the co-channel interference caused by spectrum sharing between underlaying machine-type D2D links and traditional cellular user equipments (CUEs). Therefore, we consider joint mode selection, radio resource allocation and power control to improve overall system data rate and reduce average traffic delay. We first formulate a problem to maximise the sum of users' utilities with signal-to-interference-plus-noise and power constraints for both D2D links and CUEs. Furthermore, we adopt the coalition formation game with transferable utility to solve the formulated problem. In the game model, the D2D links and CUEs that share the same resource block pair form a coalition, and the coalition formation decisions are determined by the best-reply rule. In addition, 'experimentation' is introduced in the coalition formation process to improve the effectiveness of the final coalition structure. Simulation results show that the proposed scheme outperforms the scheme with heuristic greedy algorithm in terms of overall system throughput, average traffic delay and users' fairness. Copyright
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