Dong Hoon Shin

Toward optimal allocation of location dependent tasks in crowdsensing

Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme.

Full-View Area Coverage in Camera Sensor Networks: Dimension Reduction and Near-Optimal Solutions

We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the “contribution” of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis.

Optimal monitoring in multi-channel multi-radio wireless mesh networks

Wireless mesh networks (WMN) are finding increasing usage in city-wide deployments for providing network connectivity. Mesh routers in WMNs typically use multiple wireless channels to enhance the spatial-reuse of frequency bands, often with multiple radios per node. Due to the cooperative nature of WMNs, they are susceptible to many attacks that cannot be defeated by using traditional cryptographic mechanisms of authentication or encryption alone. A solution approach commonly used for defending against such attacks is behavior-based detection in which some nodes overhear communication in their neighborhood to determine if the behavior by a neighbor is legitimate. It has been proposed to use specialized monitoring nodes deployed strategically throughout the network for performing such detection. The problem that arises is where to deploy these monitoring nodes, how to minimize their number, and which channels to tune their radios to, such that the maximum part of the network can be covered. This problem has been solved for single channel networks by a greedy approximation algorithm since the exact solution is NP-hard. The greedy algorithm achieves the best performance, in terms of the worst case, possible among all polynomial-time algorithms provided that P!=NP. In this paper, we solve the problem for multi-channel multi-radio WMNs. The intuitive extension of the greedy algorithm destroys the property of best performance. Instead, we formulate the problem as an integer linear program, solve its linear program relaxation, and then use two rounding techniques that we develop by adapting existing rounding schemes. We thereby present two approximation algorithms. The first, computationally-light algorithm, called probabilistic rounding algorithm gives an expected best performance in the worst case. The second, called deterministic rounding algorithm achieves the best worst-case performance in a deterministic manner. To evaluate how the three algorithms perform in practice, we simulate them in random networks and scale-free networks.

Distributed mobility management for efficient video delivery over all-IP mobile networks: Competing approaches

The recent proliferation of multimedia mobile devices and a variety of mobile applications are generating an enormous amount of data traffic over mobile networks. The key driver of the mobile traffic growth is mobile video. Currently, mobile networks are evolving to the 4G system, which has a flatter architecture and provides all-IP-based mobile broadband service. In all-IP mobile networks, IP mobility management is a key function that allows mobile nodes to continue their communications even when their point of attachment to the IP network changes. Existing mobile networks employ a centralized mobility management scheme where all intelligence is concentrated in one end-point system, rather than being distributed through the internet. However, this cannot satisfactorily support mobile videos, which demand a large volume of data and often require QoS such as session continuity and low delay. This motivates distributed mobility management (DMM) solutions that can efficiently handle mobile video traffic. In this article, we survey different approaches for DMM in standards development organizations such as IETF and 3GPP, and also in research organizations. We focus on three different DMM approaches that are currently being considered by the IETF: PMIPv6-based, MIPv6-based, and routing-based DMMs. We provide a qualitative analysis to compare the three DMM approaches and discuss which DMM approaches are more suitable for efficient mobile video delivery.

Near-Optimal Allocation Algorithms for Location-Dependent Tasks in Crowdsensing

Crowdsensing offers an efficient way to meet the demand in large-scale sensing applications. In crowdsensing, optimal task allocation is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations, and mobile users have time constraints. We show that the allocation problem is NP-hard. We then focus on approximation algorithms and devise an efficient local-ratio-based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We extend the results to the general scenario where mobile users are heterogeneous. A distributed version of LRBA, namely DLRBA, is designed, which can be iteratively executed at each mobile user without the need for the platform to collect all the information of mobile users. We prove that both centralized and distributed versions can output the same solution. Extensive simulation results are provided to demonstrate the advantages of our proposed algorithms.

Cascading effects in interdependent networks

Modern systems are increasingly dependent upon and interacting with each other, and become interdependent networks. These interdependent networks may exhibit some interesting and even surprising behaviors due to the interdependency and the interplay between the constituent systems. In this article we focus on two important phenomena, namely cascading failure in cyber-physical systems (CPS) and information cascade in coupled social networks. Specifically, cascading failures may occur in CPS that exhibit functional interdependency between two constituent systems (e.g. smart grid); information cascade may happen in multiple social networks that are coupled together by so-called multi-membership individuals. This article explores these two types of cascading effects in interdependent networks by reviewing existing studies in the literature. We review different models in the literature to study the two types of cascading effects in interdependent networks, and highlight the key findings from these studies.

An optimization framework for monitoring multi-channel multi-radio wireless mesh networks

This paper studies an optimal monitoring problem for behavior-based detection in multi-channel multi-radio wireless mesh networks. In behavior-based detection, nodes overhear communications in their neighborhood to determine if the behaviors of their neighbors are legitimate. The objective of this work is to maximize the number of nodes being monitored by judiciously choosing a set of monitoring nodes and also channels for the chosen monitoring nodes. This problem is NP-hard, growing exponentially with the number of monitoring nodes. We develop three approximation algorithms, each of which achieves at least a constant factor of the optimum. Furthermore, one of our algorithms achieves the best possible approximation ratio among all polynomial-time algorithms, unless P=NP. We conduct simulations in random networks and scale-free networks to evaluate the coverage and the execution time of the three algorithms.

Robust and cost-effective design of cyber-physical systems: an optimal middleware deployment approach

Cyber-Physical Systems (CPS) are emerging as the underpinning technology for major industries in this century. Wide-area monitoring and control is an essential ingredient of CPS to ensure reliability and security. Traditionally, a hierarchical system has been used to monitor and control remote devices deployed in a large geographical region. However, a general consensus is that such a hierarchical system can be highly vulnerable to component (i.e., nodes and links) failures, calling for a robust and cost-effective communication system for CPS. To this end, we consider a middleware approach to leverage the existing commercial communication infrastructure (e.g., Internet and cellular networks) with abundant connectivity. In this approach, a natural question is how to use the middleware to cohesively “glue” the physical system and the commercial communication infrastructure together, in order to enhance robustness and cost-effectiveness. We tackle this problem while taking into consideration two different cases of middleware deployment: single-stage and multi-stage deployments. We design offline and online algorithms for these two cases, respectively. We show that the offline algorithm achieves the best possible approximation ratio while the online algorithm attains the order-optimal competitive ratio. We also demonstrate the performance of our proposed algorithms through simulations.

Toward optimal sniffer-channel assignment for reliable monitoring in multi-channel wireless networks

This paper studies the optimal sniffer-channel assignment for reliable monitoring in multi-channel wireless networks. This problem concerns how to deploy certain sniffers in a network (and tune their channels) so that they can overhear and verify communication among the other nodes, referred to as normal nodes. Prior works have studied the optimal sniffer-channel assignment, but they assume perfect sniffers. However, in practice, sniffers may probabilistically make errors in monitoring, e.g., due to poor reception and compromise by an adversary. Hence, to maintain acceptable monitoring quality, a node needs to be overheard by multiple sniffers. We show that the optimal sniffer-channel assignment with sniffer redundancy differs fundamentally from the previous works due to the absence of a desirable property called submodularity. As a result, in our problem, the prior approximation algorithms no longer maintain their performance guarantees. We propose a variety of approximation algorithms based on two approaches-greedy strategy and relaxation-and-rounding approach. We present an empirical performance analysis of the proposed algorithms through simulations in practical networks. Our results suggest that our two algorithms show a performance trade-off between coverage and running time and are therefore suitable for different kinds of deployment.

From Social Group Utility Maximization to Personalized Location Privacy in Mobile Networks

With increasing popularity of location-based services (LBSs), there have also been growing concerns for location privacy. To protect location privacy in an LBS, mobile users in physical proximity can work in concert to collectively change their pseudonyms, in order to hide spatial-temporal correlation in their location traces. In this paper, we leverage mobile users’ social tie structure to motivate them to participate in pseudonym change. Drawing on a social group utility maximization framework, we cast users’ decision making of whether to change pseudonyms as a socially aware pseudonym change game (SA-PCG). The SA-PCG further assumes a general anonymity model that allows a user to have its specific anonymity set for personalized location privacy. For the SA-PCG, we show that there exists a socially aware Nash equilibrium (SNE), and quantify the system efficiency of SNEs with respect to the optimal social welfare. Then, we develop a greedy algorithm that myopically determines users’ strategies, based on the social group utility derived from only the users whose strategies have already been determined. We show that this algorithm efficiently finds an SNE that enjoys desirable properties: 1) it is socially aware coalition-proof, and thus is also Pareto-optimal; 2) it achieves higher social welfare than any SNE for the socially oblivious pseudonym change game. We further quantify the system efficiency of this SNE with respect to the optimal social welfare. We also show that this SNE can be achieved in a distributed manner. Numerical results using real data corroborate that social welfare can be significantly improved by exploiting social ties.