Broadcast disks: data management for asymmetric communication environments
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An analysis of selective tuning schemes for nonuniform broadcast
Data & Knowledge Engineering
Adaptive Data Broadcast in Hybrid Networks
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Energy efficient filtering of nonuniform broadcast
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
An Energy-Efficient and Access Latency Optimized Indexing Scheme for Wireless Data Broadcast
IEEE Transactions on Knowledge and Data Engineering
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fast data access and energy-efficient protocol for wireless data broadcast
Wireless Communications & Mobile Computing
| Hi-index | 0.00 |
In a wireless environment, a server periodically broadcasts data to users under an assumption that accurate access frequencies are known. The server broadcasts frequently accessed data more often in a broadcast cycle. The difficulty of obtaining such access frequencies is that, in a wireless environment, mobile users are only listening to the channel they are interested in and do not request the data items from the server. An approach in the literature is to make use of broadcast misses to understand the access patterns. In brief, mobile users may decide whether to wait for the required item to arrive (with uncertainty whether it will arrive soon or not) or to make an explicit request for it even though it will be broadcasted soon. In this paper, we extend our early work on access frequency estimations. First, we consider two cases: (a) a mobile user will make an explicit request when he/she cannot access the information immediately, and (b) a mobile user will make an explicit request with an arbitrary probability if he/she cannot access the information immediately. We assume that the probability is unknown. Second, we provide solutions using maximum likelihood estimation. Third, we prove λ-consistencies and m-consistencies of our solutions. We report our simulation study that demonstrates the effectiveness of our approach.