TWIST: a scalable and reconfigurable testbed for wireless indoor experiments with sensor networks
REALMAN '06 Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality
MoteLab: a wireless sensor network testbed
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
The Case for VM-Based Cloudlets in Mobile Computing
IEEE Pervasive Computing
An empirical study of low-power wireless
ACM Transactions on Sensor Networks (TOSN)
MAUI: making smartphones last longer with code offload
Proceedings of the 8th international conference on Mobile systems, applications, and services
Augmenting mobile 3G using WiFi
Proceedings of the 8th international conference on Mobile systems, applications, and services
Energy efficiency of mobile clients in cloud computing
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
A first look at problems in the cloud
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
The impact of network topology on collection performance
EWSN'11 Proceedings of the 8th European conference on Wireless sensor networks
Minimum-Delay Service Provisioning in Opportunistic Networks
IEEE Transactions on Parallel and Distributed Systems
Modeling and simulation of service composition in opportunistic networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
On context awareness and social distance in human mobility traces
Proceedings of the third ACM international workshop on Mobile Opportunistic Networks
Opportunistic networking: data forwarding in disconnected mobile ad hoc networks
IEEE Communications Magazine
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With opportunistic computing, devices are no longer restricted to using their own services and resources, but can access services and resources made available by other devices. The performance of opportunistic computing is greatly affected by the resource topology in the network: what resources/services are available, as well as when and where they can be tapped. This paper presents a preliminary investigation of the impact of the resource availability on the performance of opportunistic computing. Specifically, we propose a metric called Expected Resource Availability, ERA, that attempts to capture the impact of the topology of services and resources. The ERA offers a proxy for the applicability of opportunistic computing schemes to a given network: if the ERA is low, any opportunistic scheme can be expected to fail due to a sheer lack of resources and/or connectivity among them. On the other hand, if the ERA is high, success can be expected. To gain perspective on the properties of the ERA, we tackle the problem of service allocation in opportunistic computing, which suffers to combinatorial explosion when looking for the optimal solution. We also present some preliminary simulation results that confirm the validity of the ERA as a metric to gauge whether opportunistic computing can be achieved in a given network.