Tie Luo

Sensor OpenFlow: Enabling Software-Defined Wireless Sensor Networks

While it has been a belief for over a decade that wireless sensor networks (WSN) are application-specific, we argue that it can lead to resource underutilization and counter-productivity. We also identify two other main problems with WSN: rigidity to policy changes and difficulty to manage. In this paper, we take a radical, yet backward and peer compatible, approach to tackle these problems inherent to WSN. We propose a Software-Defined WSN architecture and address key technical challenges for its core component, Sensor OpenFlow. This work represents the first effort that synergizes software-defined networking and WSN.

Profit-Maximizing Incentive for Participatory Sensing

We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we formulate as a profit-maximization problem. We use a contribution-dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and weakly risk-averse agents. The thus induced profit is demonstrated to be higher than the maximum profit induced by constant (yet optimized) prize. We also show that our results are readily extensible to cases of risk-neutral agents and deterministic populations.

Cooperative Asynchronous Multichannel MAC: Design, Analysis, and Implementation

Medium access control (MAC) protocols have been studied under different contexts for decades. In decentralized contexts, transmitter-receiver pairs make independent decisions, which are often suboptimal due to insufficient knowledge about the communication environment. In this paper, we introduce distributed information sharing (DISH), which is a distributed flavor of control-plane cooperation, as a new approach to wireless protocol design. The basic idea is to allow nodes to share control information with each other such that nodes can make more informed decisions in communication. This notion of control-plane cooperation augments the conventional understanding of cooperation, which sits at the data plane as a data relaying mechanism. In a multichannel network, DISH allows neighboring nodes to notify transmitter-receiver pairs of channel conflicts and deaf terminals to prevent collisions and retransmissions. Based on this, we design a single-radio cooperative asynchronous multichannel MAC protocol called CAM-MAC. For illustration and evaluation purposes, we choose a specific set of parameters for CAM-MAC First, our analysis shows that its throughput upper bound is 91 percent of the system bandwidth and our simulations show that it actually achieves a throughput of 96 percent of the upper bound. Second, our analysis shows that CAM-MAC can saturate 15 channels at maximum and our simulations show that it saturates 14.2 channels on average, which indicates that, although CAM-MAC uses a control channel, it does not realistically suffer from control channel bottleneck. Third, we compare CAM-MAC with its noncooperative version called UNCOOP, and observe a throughput ratio of 2.81 and 1.70 in single-hop and multihop networks, respectively. This demonstrates the value of cooperation. Fourth, we compare CAM-MAC with three recent multichannel MAC protocols, MMAC, SSCH, and AMCP, and find that CAM-MAC significantly outperforms all of them. Finally, we implement CAM-MAC and UNCOOP on commercial off-the-shelf hardware and share lessons learned in the implementation. The experimental results confirm the viability of CAM-MAC and the idea of DISH.

CAM-MAC: A Cooperative Asynchronous Multi-Channel MAC Protocol for Ad Hoc Networks

Medium access control (MAC) protocols have been studied under different contexts for several years now. In all these MAC protocols, nodes make independent decisions on when to transmit a packet and when to back-off from transmission. In this paper, we introduce the notion of node cooperation into MAC protocols. Cooperation adds a new degree of freedom which has not been explored before. Specifically we study the design of cooperative MAC protocols in an environment where each node is equipped with a single transceiver and has multiple channels to choose from. Nodes cooperate by helping each other select a free channel to use. We show that this simple idea of cooperation has several qualitative and quantitative advantages. Our cooperative asynchronous multi-channel MAC protocol (CAM-MAC) is extremely simple to implement and, unlike other multi-channel MAC protocols, is naturally asynchronous. We conduct extensive simulation experiments. We first compare CAM-MAC with IEEE 802.11b and a version of CAM-MAC with the cooperation element removed. We use this to show the value of cooperation. Our results show significant improvement in terms of number of collisions and throughput for CAM-MAC. We also compare our protocol with MMAC and SSCH and show that CAM-MAC significantly outperforms both of them.

Network architecture and QoS issues in the internet of things for a smart city

The emerging Internet of Things (IoT) that effectively integrates cyber-physical space to create smart environments will undoubtedly have a plethora of applications in the near future. Meanwhile, it is also the key technological enabler to create smart cities, which will provide great benefits to our society. In this paper, four different IoT network architectures spanning various smart city applications are presented and their corresponding network Quality of Service (QoS) requirements are defined. Furthermore, as the beneficiary of smart city, we have the responsibility to actively participate in its development as well. A new network paradigm, participatory sensing, is thus discussed as a special case to highlight the way people may be involved in the information acquisition-transmission-interpretation-action loop.

Sensing-Driven Energy Purchasing in Smart Grid Cyber-Physical System

Distributed and renewable-energy resources are likely to play an important role in the future energy landscape as consumers and enterprise energy users reduce their reliance on the main electricity grid as their source of electricity. Environmental or ambient sensing of parameters such as temperature and humidity, and amount of sunlight and wind, can be used to predict electricity demand from users and supply from renewable sources, respectively. In this paper, we describe a Smart Grid Cyber-Physical System (SG-CPS) comprising sensors that transmit real-time streams of sensed information to predictors of demand and supply of electricity and an optimization-based decision maker that uses these predictions together with real-time grid electricity prices and historical information to determine the quantity and timing of grid electricity purchases throughout the day and night. We investigate two forms of the optimization-based decision maker, one that uses linear programming and another that uses multi-stage stochastic programming. Our results show that sensing-driven predictions combined with the optimization-based purchasing decision maker hosted on the SG-CPS platform can cope well with uncertainties in demand, supply, and electricity prices and make grid electricity purchasing decisions that successfully keep both the occurrence of electricity shortfalls and the cost of grid electricity purchases low. We then examine the computational and memory requirements of the aforementioned prediction and optimization algorithms and find that they are within the capabilities of modern embedded system microprocessors and, hence, are amenable for deployment in typical households and communities.

SEW-ing a Simple Endorsement Web to Incentivize Trustworthy Participatory Sensing

Two crucial issues to the success of participatory sensing are (a) how to incentivize the large crowd of mobile users to participate and (b) how to ensure the sensing data to be trustworthy. While they are traditionally being studied separately in the literature, this paper proposes a Simple Endorsement Web (SEW) to address both issues in a synergistic manner. The key idea is (a) introducing a social concept called nepotism into participatory sensing, by linking mobile users into a social “web of participants” with endorsement relations, and (b) overlaying this network with investment-like economic implications. The social and economic layers are interleaved to provision and enhance incentives and trustworthiness. We elaborate the social implications of SEW, and analyze the economic implications under a Stackelberg game framework. We derive the optimal design parameter that maximizes the utility of the sensing campaign organizer, while ensuring participants to strictly have incentive to participate. We also design algorithms for participants to optimally “sew” SEW, namely to manipulate the endorsement links of SEW such that their economic benefits are maximized and social constrains are satisfied. Finally, we provide two numerical examples for an intuitive understanding.

Quality of Contributed Service and Market Equilibrium for Participatory Sensing

User-contributed or crowd-sourced information is becoming increasingly common. In this paper, we consider the specific case of participatory sensing whereby people contribute information captured by sensors, typically those on a smartphone, and share the information with others. We propose a new metric called quality of contributed service (QCS) which characterizes the information quality and timeliness of a specific real-time sensed quantity achieved in a participatory manner. Participatory sensing has the problem that contributions are sporadic and infrequent. To overcome this, we formulate a market-based framework for participatory sensing with plausible models of the market participants comprising data contributors, service consumers and a service provider. We analyze the market equilibrium and obtain a closed form expression for the resulting QCS at market equilibrium. Next, we examine the effects of realistic behaviors of the market participants and the nature of the market equilibrium that emerges through extensive simulations. Our results show that, starting from purely random behavior, the market and its participants can converge to the market equilibrium with good QCS within a short period of time.

Incentive Mechanism Design for Crowdsourcing: An All-Pay Auction Approach

Crowdsourcing can be modeled as a principal-agent problem in which the principal (crowdsourcer) desires to solicit a maximal contribution from a group of agents (participants) while agents are only motivated to act according to their own respective advantages. To reconcile this tension, we propose an all-pay auction approach to incentivize agents to act in the principal’s interest, i.e., maximizing profit, while allowing agents to reap strictly positive utility. Our rationale for advocating all-pay auctions is based on two merits that we identify, namely all-pay auctions (i) compress the common, two-stage “bid-contribute” crowdsourcing process into a single “bid-cum-contribute” stage, and (ii) eliminate the risk of task nonfulfillment. In our proposed approach, we enhance all-pay auctions with two additional features: an adaptive prize and a general crowdsourcing environment. The prize or reward adapts itself as per a function of the unknown winning agent’s contribution, and the environment or setting generally accommodates incomplete and asymmetric information, risk-averse (and risk-neutral) agents, and a stochastic (and deterministic) population. We analytically derive this all-pay auction-based mechanism and extensively evaluate it in comparison to classic and optimized mechanisms. The results demonstrate that our proposed approach remarkably outperforms its counterparts in terms of the principal’s profit, agent’s utility, and social welfare.

Fairness and social welfare in service allocation schemes for participatory sensing

Leveraging on the pervasiveness of mobile phones and their rich built-in sensors, participatory sensing recently emerged as a promising approach to large-scale data collection. Whilst some contributors may be altruistic, many contributors are motivated by receiving something in return for their contributions, proportional to their level of contributions. In this paper, we adopt a service allocation approach that motivates users by allocating a determined amount of compelling services to contributors, as an alternative to other credit or reputation based incentive approaches. To address two major concerns that would arise from this approach, namely fairness and social welfare, we propose two service allocation schemes called Allocation with Demand Fairness (ADF) and Iterative Tank Filling (ITF), which is an optimization-based approach. We show that: (i) ADF is max-min fair and scores close to 1 on the Jains fairness index, and (ii) ITF maximizes social welfare and achieves the unique Nash equilibrium, which is also Pareto and globally optimal. In addition, we use stochastic programming to extend ITF to handle uncertainty in service demands that is often encountered in real-life situations.