“WHAT can men do, what do they do, and what do they want to do ?”—these are the uestions that Prof. Thorndike seeks to answer in a very comprehensive and elaborate treatise. His undertaking is inspired by the belief that man has the possibility of almost complete control of his fate if only he will be guided by science, and that his failures are attributable to ignorance or folly. The main approach is through biological psychology, but all the social sciences are appealed to and utilized in an effort to deal with the human problem as a whole. The relative immaturity of the sciences dealing with man is continually stressed, but it is claimed that they provide a body of facts and principles which are “far above zero knowledge”, and that even now they are capable of affording valuable guidance in the shaping of public policy. Human Nature and the Social Order By E. L. Thorndike. Pp. xx + 1020. (New York: The Macmillan Company, 1940.) 18s. net.

Human Nature and the Social Order

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Stochastic Processes And Filtering Theory

A survey of distributed optimization

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Book Review: Discrete-time Markov control processes: Basic optimality criteria

Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.

Narrowband Internet of Things: Implementations and Applications

In this paper, we consider joint pricing and task allocation in a unified mobile crowdsensing system, where all task initiators and mobile users are viewed as peers. From an exchange market point of view, the pricing and task allocation in such a unified system depend only on the supply and demand since no one can dominate the process, with the optimal solution being characterized by the Walrasian equilibrium. This is quite different from existing approaches, where each task initiator builds a specific mobile crowdsensing system and provides an incentive mechanism to maximize his/her own utility. We design distributed algorithms to compute the Walrasian equilibrium under the scenario where one cloud platform is available in the system. We propose to maximize social welfare of the whole system, and dual decomposition is then employed to divide the social welfare maximization problem into a set of subproblems that can be solved by task initiators and mobile users. We prove that the proposed algorithm converges to the optimal solution of social welfare maximization problem. Further, we show that the prices and task allocation obtained by the algorithm also yields a Walrasian equilibrium. Also, the proposed algorithm does not need the cloud to collect private information such as utility functions of task initiators and cost functions of mobile users. Extensive simulations demonstrate the effectiveness of the proposed algorithms.

Distributed Algorithms to Compute Walrasian Equilibrium in Mobile Crowdsensing

Accurate clock synchronization in wireless sensor networks with bounded noise

Maximum consensus is useful in many applications such as leader selection and time synchronization, and it is advantageous for its decentralization and finite time convergence. However, without preservation mechanisms for maximum consensus, nodes' initial states and especially the identity of the node with the maximum initial state will be disclosed, which is undesirable in some application scenarios. To preserve the privacy of maximum consensus while maintaining its advantages, we propose a Privacy Preserving Maximum Consensus (PPMC) algorithm, where all nodes independently generate and transmit random numbers before sending out their initial states. We prove that PPMC converges in finite time. Meanwhile, explicit formulas are given to characterize the probability that the maximum state owner's identity is inferred by its neighbors. Extensive simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

Privacy Preserving Maximum Consensus

The DeGroot–Friedkin (DF) model is a recently proposed dynamical description of the evolution of individuals’ self-appraisal and social power in a social influence network. Most studies of this system and its variations have so far focused on models with a time-invariant influence network. This paper proposes novel models and analysis results for DF models over switching influence networks, and with or without environment noise. First, for a DF model over switching influence networks, we show that the trajectory of the social power converges to a ball centered at the equilibrium reached by the original DF model. For the DF model with memory on random interactions, we show that the social power converges to the equilibrium of the original DF model almost surely. Additionally, this paper studies a DF model that contains random interactions and environment noise, and has memory on the self-appraisal. We show that such a system converges to an equilibrium or a set almost surely. Finally, as a by-product, we provide novel results on the convergence rates of the original DF model and convergence results for a continuous-time DF model.

Social Power Dynamics Over Switching and Stochastic Influence Networks

Under consensus-based economic dispatch (ED) algorithm, multiple agents, which control local generation units, cooperatively minimize the total generation cost subject to the balance of the generation and expected demand in smart grids. As ubiquitous time delays on communication links exist in communication networks, studying the effect of delays on the dispatch performance is of both theoretical merit and practical value for the efficient and stable operation of smart grids. In this paper, we consider a well-developed consensus-based ED protocol under constant time delays. We find that there always exists a sufficiently small learning gain parameter under finite constant delays such that the convergence of the consensus-based algorithm is guaranteed. Further, an analytical expression of the upper bound is established for the learning gain parameter, which is determined by the largest delay, the weight matrix and the parameters of generation cost functions. In order to guarantee the optimality of the final solution, we propose the updating rule for iterations when initial states are not received by their neighbors due to time delays. The optimality of the final solution under the proposed updating rule is analyzed. We validate our theoretical results through extensive simulation studies.

Xiaoming Duan

Papers

Distributed Algorithms to Compute Walrasian Equilibrium in Mobile Crowdsensing

Accurate clock synchronization in wireless sensor networks with bounded noise

Privacy Preserving Maximum Consensus

Social Power Dynamics Over Switching and Stochastic Influence Networks

Analysis of Consensus-Based Economic Dispatch Algorithm Under Time Delays