This report contains revision and extension of some results about RBO from [14]. RBO is a simple and ecient broadcast scheduling of n = 2 k uniform frames for battery powered radio receivers. Each frame contains a key from some arbitrary linearly ordered universe. The broadcast cycle a sequence of frames sorted by the keys and permuted by k-bit reversal is transmitted in a round robin fashion by the broadcaster. At arbitrary time during the transmission, the receiver may start a simple protocol that reports to him all the frames with the keys that are contained in a specied
Khanna and Zhou @cite_6 proposed a sophisticated version of the index tree aimed at minimizing mean access and tuning time, for given probability of each data record being requested. The broadcast cycle contains multiple copies of data items, so that spacing between copies of each item is related to the optimal spacing, minimizing mean access time derived in @cite_18 . However the keys are not arbitrary. The key of the item is determined by its probability of being requested.
@cite_18 @cite_6
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We consider the problem of efficient information retrieval in asymmetric communication environments where multiple clients with limited resources retrieve information from a powerful server who periodically broadcasts its information repository over a communication medium. The cost of a retrieving client consists of two components: (a) access time, defined as the total amount of time spent by a client in retrieving the information of interest, and (b) tuning time, defined as the time spent by the client in actively listening to the communication medium, measuring a certain efficiency in resource usage. A probability distribution is associated with the data items in the broadcast, representing the likelihood of a data item's being requested at any point of time. The problem of indexed data broadcast is to schedule the data items interleaved with certain indexing information in the broadcast so as to minimize simultaneously the mean access time and the mean tuning time. Prior work on this problem thus far has focused only on some special cases. In this paper we study the indexed data broadcast problem in its full generality and design a broadcast scheme that achieves a mean access time of at most (1.5+?) times the optimal and a mean tuning time bounded by O(logn). In wireless mobile computing environments, broadcasting is an effective and scalable technique to disseminate information to a massive number of clients, wherein the energy usage and latency are considered major concerns. This paper presents an indexing scheme for the energy- and latency-efficient processing of full-text searches over the wireless broadcast data stream. Although a lot of access methods and index structures have been proposed in the past for full-text searches, all of them are targeted for data in disk storage, not wireless broadcast channels. For full-text searches on a wireless broadcast stream, we firstly introduce a naive, inverted list-style indexing method, where inverted lists are placed in front of the data on the wireless channel. In order to reduce the latency overhead, we propose a two-level indexing method which adds another level of index structure to the basic inverted list-style index. In addition, we propose a replication strategy of the index list and index tree to further improve the latency performance. We analyze the performance of the proposed indexing scheme with respect to the latency and energy usage measures, and show the optimality of index replication. The correctness of the analysis is demonstrated through simulation experiments, and the effectiveness of the proposed scheme is shown by implementing a real wireless information delivery system. We consider a Multi-Sender Unicast Index-Coding (MSUIC) problem, where in a broadcast network, multiple senders collaboratively send distinct messages to multiple receivers, each having some subset of the messages a priori . The aim is to find the shortest index code that minimizes the total number of coded bits sent by the senders. In this paper, built on the classic single-sender minrank concept, we develop a new rank-minimization framework for MSUIC that explicitly takes into account the sender message constraints and minimizes the sum of the ranks of encoding matrices subject to the receiver decoding requirements. This framework provides a systematic way to construct multi-sender linear index codes and to study their capability in achieving the shortest index codelength per message length (i.e., the optimal broadcast rate). In particular, we establish the optimal broadcast rate for all critical MSUIC instances with up to four receivers and show that a binary linear index code is optimal for all, except 15 instances with four receivers. We also propose a heuristic algorithm (in lieu of exhaustive search) to solve the rank-minimization problem. The effectiveness of the algorithm is validated by numerical studies of MSUIC instances with four or more receivers. We consider the problem of storing segments of encoded versions of content files in a set of base stations located in a communication cell. These base stations work in conjunction with the main base station of the cell. Users move randomly across the space based on a discrete-time Markov chain model. At each time slot each user accesses a single base station based on it's current position and it can download only a part of the content stored in it, depending on the time slot duration. We assume that file requests must be satisfied within a given time deadline in order to be successful. If the amount of the downloaded (encoded) data by the accessed base stations when the time deadline expires does not suffice to recover the requested file, the main base station of the cell serves the request. Our aim is to find the storage allocation that minimizes the probability of using the main base station for file delivery. This problem is intractable in general. However, we show that the optimal solution of the problem can be efficiently attained in case that the time deadline is small. To tackle the general case, we propose a distributed approximation algorithm based on large deviation inequalities. Systematic experiments on a real world data set demonstrate the effectiveness of our proposed algorithms. Index Terms: Mobility-aware Caching, Markov Chain, MDS Coding, Small-cell Networks