In this letter we propose an advanced rate adaptation algorithm that intelligently uses the channel statistics to make fast and efficient selection of transmission rates. Our implementation and simulation results prove that the proposed strategy achieves major latency and throughput improvements on 802.11n products and existing related protocols. The entire work is on a software module, thus providing adaptability, cost-effectiveness, with no hardware changes.

An information-theoretic, optimal framework is developed for tracking the residents in a Context-aware Heterogenous Smart Environment. The resident-tracking problem is formulated in terms of weighted entropy. The framework provides an optimal, online learning and prediction of users movement, location as well as most probable path segments from the symbolic domain. Successful prediction helps in on-demand operations of automated indoor devices along the users future paths and locations, thus providing the necessary comfort at a near-optimal cost. Simulation results corroborate the high prediction success, thereby providing resident-comfort while reducing energy consumption and manual operations.

A lot of emerging applications like advanced telemedicine and surveillance systems, demand sensors to deliver multimedia content with precise level of QoS enhancement. Minimizing energy in sensor networks has been a much explored research area but guaranteeing QoS over sensor networks still remains an open issue. In this letter we propose a cross-layer approach combining Network and MAC layers, for QoS enhancement in wireless multimedia sensor networks. In the network layer a statistical estimate of sensory QoS parameters is performed and a near-optimal genetic algorithmic solution is proposed to solve the NP-complete QoS-routing problem. On the other hand the objective of the proposed MAC algorithm is to perform the QoS-based packet classification and automatic adaptation of the contention window. Simulation results demonstrate that the proposed protocol is capable of providing lower delay and better throughput, at the cost of reasonable energy consumption, in comparison with other existing sensory QoS protocols.

In this letter we propose a novel resource allocation and admission control strategy for OFDMA-based emerging LTE systems. Considering users' reneging and migration between service providers, we first prove that the optimal resource allocation problem, which maximizes the service provider's gross income is, NP-complete. Subsequently, we propose two different heuristics based on dynamic programming and greedy algorithms to get a near-optimal resource allocation and admission control strategy in computationally feasible time. Simulation results point out that the solutions offer increased gross income of the service provider, while offering low latency, adequate throughput and session acceptance.

Continuous processing and servicing of a new incoming session in LTE systems demands optimal tracking of the mobile user. In this letter, an optimal, information-theoretic framework is developed for tracking area update for next-generation LTE cellular systems. Shannon's entropy is used to characterize the location uncertainty of mobile user. The framework captures users' mobility patterns online and performs profile-based paging for optimizing the tracking area update cost. Simulation results demonstrate reductions in both update and paging costs in comparison to existing LTE systems.

In this letter we show that the dynamic optimal PCID allocation problem in LTE systems is NP-complete. Subsequently we provide a near-optimal solution using SON which models the problem using new merge operations and explores the search space using a suitable randomized algorithmic approach. Two feasible options for dynamic auto-configuration of the system are also discussed. Simulation results point out that the approach provides near-optimal auto-configuration of PCIDs in computationally feasible time.