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Richard J. La

Publications and source records attributed to Richard J. La.

22 records · Page 2Linked to original sources

Internalization of Externalities in Interdependent Security: Large Network Cases

With increasing connectivity among comprising agents or (sub-)systems in large, complex systems, there is a growing interest in understanding interdependent security and dealing with inefficiency in security investments. Making use of a population game model and the well-known Chung-Lu random graph model, we study how one could encourage selfish agents to invest more in security by internalizing the externalities produced by their security investments. To this end, we first establish an interesting relation between the local minimizers of social cost and the Nash equilibria of a population game with slightly altered costs. Secondly, under a mild technical assumption, we demonstrate that there exists a unique minimizer of social cost and it coincides with the unique Nash equilibrium of the population game. This finding tells us how to modify the private cost functions of selfish agents in order to enhance the overall security and reduce social cost. In addition, it reveals how the sensitivity of overall security to security investments of agents influences their externalities and, consequently, penalties or taxes that should be imposed for internalization of externalities. Finally, we illustrate how the degree distribution of agents influences their security investments and overall security at both the NEs of population games and social optima.

cs.SI↗

Interdependent Security with Strategic Agents and Cascades of Infection

We investigate cascades in networks consisting of strategic agents with interdependent security. We assume that the strategic agents have choices between i) investing in protecting themselves, ii) purchasing insurance to transfer (some) risks, and iii) taking no actions. Using a population game model, we study how various system parameters, such as node degrees, infection propagation rate, and the probability with which infected nodes transmit infection to neighbors, affect nodes' choices at Nash equilibria and the resultant price of anarchy/stability. In addition, we examine how the probability that a single infected node can spread the infection to a significant portion of the entire network, called cascade probability, behaves with respect to system parameters. In particular, we demonstrate that, at least for some parameter regimes, the cascade probability increases with the average degree of nodes.

cs.SI↗

Fast Adaptive S-ALOHA Scheme for Event-driven M2M Communications (Journal version)

Supporting massive device transmission is challenging in Machine-to-Machine (M2M) communications. Particularly, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for random access control of M2M communication systems with bursty traffic. Instead of the observation in a single slot, the statistics of consecutive idle and collision slots are used in FASA to accelerate the tracking process of network status which is critical for optimizing S-ALOHA systems. Using drift analysis, we design the FASA scheme such that the estimate of the backlogged devices converges fast to the true value. Furthermore, by examining the $T$-slot drifts, we prove that the proposed FASA scheme is stable as long as the average arrival rate is smaller than $e^{-1}$, in the sense that the Markov Chain derived from the scheme is geometrically ergodic. Simulation results demonstrate that the proposed FASA scheme outperforms traditional additive schemes such as PB-ALOHA and achieves near-optimal performance in reducing access delay. Moreover, compared to multiplicative schemes, FASA shows its robustness under heavy traffic load in addition to better delay performance.

cs.IT↗

Fast Adaptive S-ALOHA Scheme for Event-driven Machine-to-Machine Communications

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestions and severe access delay. To address this issue, we propose a Fast Adaptive S-ALOHA (FASA) scheme for M2M communication systems with bursty traffic. The statistics of consecutive idle and collision slots, rather than the observation in a single slot, are used in FASA to accelerate the tracking process of network status. Furthermore, the fast convergence property of FASA is guaranteed by using drift analysis. Simulation results demonstrate that the proposed FASA scheme achieves near-optimal performance in reducing access delay, which outperforms that of traditional additive schemes such as PB-ALOHA. Moreover, compared to multiplicative schemes, FASA shows its robustness even under heavy traffic load in addition to better delay performance.

cs.IT↗