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International audienceThe potential availability of storage space at cellular and femtocell base-stations (BSs) raises the following question: How should one optimize performance through both load balancing and content replication when requests can be sent to several such BSs? We formally introduce an optimization model to address this question and propose an online algo- rithm for dynamic caching and request assignment. Cru- cially our request assignment scheme is based on a server price signal that jointly reflects content and bandwidth avail- ability. We prove that our algorithm is optimal and sta- ble in a limiting regime that is obtained by scaling the ar- rival rates and content chunking. From an implementation standpoint, guided by the online algorithm we design a light- weight scheme for request assignments that is based on load and cache-miss cost signals; for cache replacements, we pro- pose to use the popular LRU (Least Recently Used) strat- egy. Through simulations, we exhibit the efficacy of our joint-price based request assignment strategy in comparison to the common practices of assigning requests purely based on either bandwidth availability or content availability

We consider a wireless sensor network whose main function is to detect certain infrequent alarm events, and to forward alarm packets to a base station, using geographical forwarding. The nodes know their locations, and they sleepwake cycle, waking up periodically but not synchronously. In this situation, when a node has a packet to forward to the sink, there is a trade-off between how long this node waits for a suitable neighbor to wake up and the progress the packet makes towards the sink once it is forwarded to this neighbor. Hence, in choosing a relay node, we consider the problem of minimizing average delay subject to a constraint on the average progress. By constraint relaxation, we formulate this next hop relay selection problem as a Markov decision process (MDP). The exact optimal solution (BF (Best Forward)) can be found, but is computationally intensive. Next, we consider a mathematically simplified model for which the optimal policy (SF (Simplified Forward)) turns out to be a simple one-step-look-ahead rule. Simulations show that SF is very close in performance to BF, even for reasonably small node density. We then study the end-to-end performance of SF in comparison with two extremal policies: Max Forward (MF) and First Forward (FF), and an end-to-end delay minimising policy proposed by Kim et al. [1]. We find that, with appropriate choice of one hop average progress constraint, SF can be tuned to provide a favorable trade-off between end-to-end packet delay and the number of hops in the forwarding path.

Our work is motivated by the need for impromptu (or "as-you-go") deployment of relay nodes (for establishing a packet communication path with a control centre) by firemen/commandos while operating in an unknown environment. We consider a model, where a deployment operative steps along a random lattice path whose evolution is Markov. At each step, the path can randomly either continue in the same direction or take a turn "North" or "East," or come to an end, at which point a data source (e.g., a temperature sensor) has to be placed that will send packets to a control centre at the origin of the path. A decision has to be made at each step whether or not to place a wireless relay node. Assuming that the packet generation rate by the source is very low, and simple link-by-link scheduling, we consider the problem of relay placement so as to minimize the expectation of an end-to-end cost metric (a linear combination of the sum of convex hop costs and the number of relays placed). This impromptu relay placement problem is formulated as a total cost Markov decision process. First, we derive the optimal policy in terms of an optimal placement set and show that this set is characterized by a boundary beyond which it is optimal to place. Next, based on a simpler alternative one-step-look-ahead characterization of the optimal policy, we propose an algorithm which is proved to converge to the optimal placement set in a finite number of steps and which is faster than the traditional value iteration. We show by simulations that the distance based heuristic, usually assumed in the literature, is close to the optimal provided that the threshold distance is carefully chosen.