Demo Data from EDBT 2026 / reviewers in the wild / expert
Alireza Aram
dblp:90/7890
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 3 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
3 papers |
Network optimization and economics · 90% Edge and fog computing · 8% Wireless networking · 2% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics › game theory
game-theoretic networking |
0.4 | 3 | 2012 | Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks · IEEE/ACM Trans. Netw. 2012 Provider-Customer Coalitional Games · IEEE/ACM Trans. Netw. 2011 Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks · INFOCOM 2009 |
Network optimization and economics
resource allocation |
0.4 | 3 | 2012 | Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks · IEEE/ACM Trans. Netw. 2012 Provider-Customer Coalitional Games · IEEE/ACM Trans. Netw. 2011 Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks · INFOCOM 2009 |
Network optimization and economics › game theory › cooperative game theory
coalitional game |
0.3 | 2 | 2012 | Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks · IEEE/ACM Trans. Netw. 2012 Provider-Customer Coalitional Games · IEEE/ACM Trans. Netw. 2011 |
Network optimization and economics › game theory
cooperative game theory |
0.1 | 1 | 2009 | Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks · INFOCOM 2009 |
Edge and fog computing › resource management
cooperative resource allocation |
0.1 | 1 | 2009 | Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks · INFOCOM 2009 |
Wireless networking
wireless access |
0.0 | 1 | 2009 | Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks · INFOCOM 2009 |
Methods — techniques the papers use, named apart from their topics
coalitional game theory · 0.2integer optimization · 0.1duality · 0.1concave optimization · 0.1exchange market equilibrium · 0.1convex optimization · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless NetworksabstractWe consider a network in which several service providers offer wireless access to their respective subscribed customers through potentially multihop routes. If providers cooperate by jointly deploying and pooling their resources, such as spectrum and infrastructure (e.g., base stations) and agree to serve each others' customers, their aggregate payoffs, and individual shares, may substantially increase through opportunistic utilization of resources. The potential of such cooperation can, however, be realized only if each provider intelligently determines with whom it would cooperate, when it would cooperate, and how it would deploy and share its resources during such cooperation. Also, developing a rational basis for sharing the aggregate payoffs is imperative for the stability of the coalitions. We model such cooperation using the theory of transferable payoff coalitional games. We show that the optimum cooperation strategy, which involves the acquisition, deployment, and allocation of the channels and base stations (to customers), can be computed as the solution of a concave or an integer optimization. We next show that the grand coalition is stable in many different settings, i.e., if all providers cooperate, there is always an operating point that maximizes the providers' aggregate payoff, while offering each a share that removes any incentive to split from the coalition. The optimal cooperation strategy and the stabilizing payoff shares can be obtained in polynomial time by respectively solving the primals and the duals of the above optimizations, using distributed computations and limited exchange of confidential information among the providers. Numerical evaluations reveal that cooperation substantially enhances individual providers' payoffs under the optimal cooperation strategy and several different payoff sharing rules. Chandramani Kishore Singh, Saswati Sarkar, Alireza Aram, Anurag Kumar 0001 |
IEEE/ACM Trans. Netw. | 3 |
| 2011 | Provider-Customer Coalitional GamesabstractEfficacy of commercial wireless networks can be substantially enhanced through large-scale cooperation among involved entities such as providers and customers. The success of such cooperation is contingent upon the design of judicious resource allocation strategies that ensure that the individuals' payoffs are commensurate to the resources they offer to the coalition. The resource allocation strategies depend on which entities are decision-makers and whether and how they share their aggregate payoffs. Initially, we consider the scenario where the providers are the only decision-makers and they do not share their payoffs. We formulate the resource allocation problem as a nontransferable payoff coalitional game and show that there exists a cooperation strategy that leaves no incentive for any subset of providers to split from the grand coalition, i.e., the core of the game is nonempty. To compute this cooperation strategy and the corresponding payoffs, we subsequently relate this game and its core to an exchange market setting and its equilibrium, which can be computed by several efficient algorithms. Next, we investigate cooperation when customers are also decision-makers and decide which provider to subscribe to based on whether there is cooperation. We formulate a coalitional game in this setting and show that it has a nonempty core. Finally, we extend the formulations and results to the cases where the payoffs are vectors and can be shared selectively. Chandramani Kishore Singh, Saswati Sarkar, Alireza Aram |
IEEE/ACM Trans. Netw. | 3 |
| 2009 | Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless NetworksabstractWe consider a network in which several service providers offer wireless access service to their respective subscribed customers through potentially multi-hop routes. If providers cooperate, i.e., pool their resources, such as spectrum and base stations, and agree to serve each others' customers, their aggregate payoffs, and individual shares, can potentially substantially increase through efficient utilization of resources and statistical multiplexing. The potential of such cooperation can however be realized only if each provider intelligently determines who it would cooperate with, when it would cooperate, and how it would share its resources during such cooperation. Also, when the providers share their aggregate revenues, developing a rational basis for such sharing is imperative for the stability of the coalitions. We model such cooperation using transferable payoff coalitional game theory. We first consider the scenario that locations of the base stations and the channels that each provider can use have already been decided apriori. We show that the optimum cooperation strategy, which involves the allocations of the channels and the base stations to mobile customers, can be obtained as solutions of convex optimizations. We next show that the grand coalition is stable in this case, i.e. if all providers cooperate, there is always an operating point that maximizes the providers' aggregate payoff, while offering each such a share that removes any incentive to split from the coalition. Next, we show that when the providers can choose the locations of their base stations and decide which channels to acquire, the above results hold in important special cases. Finally, we examine cooperation when providers do not share their payoffs, but still share their resources so as to enhance individual payoffs. We show that the grand coalition continues to be stable. Alireza Aram, Chandramani Kishore Singh, Saswati Sarkar, Anurag Kumar 0001 |
INFOCOM | 1 |