Hisham M. Almasaeid

Modeling Mobility-Assisted Data Collection in Wireless Sensor Networks

Exploiting mobility to enhance the performance of wireless sensor networks (WSNs), in terms of connectivity, coverage, and lifetime elongation, has recently been the focus of several research studies. Mobility was adopted in two different ways; either using a network of mobile sensor nodes or deploying a few supplementary special mobile elements, usually referred to as mobile agents to help enhance connectivity and coverage. Different modes of operation (roles) were assigned to mobile agents including being a data relay, data collector, and data sink. In this paper we use a closed queueing network to model mobility and then evaluate data latency under all those roles. The proposed model provides powerful means to understand the effect of different parameters, like velocity and number of mobile agents as well as their movement strategy, on data latency.

On-Demand Multicast Routing in Cognitive Radio Mesh Networks

Cognitive radio networks (CRN) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. In this work, we consider the multicast routing and channel allocation problem in cognitive radio mesh networks. Due to the potential heterogeneity in channel availability among mesh routers (MRs) and the frequency switching latency, end-to-end delay and throughput degradation could be subject to a significant increase. We propose an on-demand multicast routing and channel allocation algorithm that takes channel heterogeneity and switching latency into consideration. The algorithm aims at reducing the end-to-end delay, and at the same time reducing the degradation of throughput using a dynamic programming approach.

Exploiting multichannel diversity for cooperative multicast in cognitive radio mesh networks

Cognitive radio networks (CRNs) have emerged as a promising, yet challenging, solution to enhance spectrum utilization, thanks to the technology of cognitive radios. A well-known property of CRNs is the potential heterogeneity in channel availability among secondary users. Therefore, multicast throughput in CRNs may suffer from significant degradation because of this property since a link-level broadcast of a frame may only reach a small subset of destinations that are able to receive on the same channel. This may necessitate multiple sequential transmissions of the same frame by the source on different channels to guarantee delivery to all receivers in the destination set. In case of high data generation rate, delivery delay will be high due to the repeated transmissions by the source. In this paper, we propose an assistance strategy to reduce the effect of the channel heterogeneity property on the multicast throughput in cognitive radio wireless mesh networks (CR-WMNs). This assistance strategy is composed of two main activities: first, allowing multicast receivers to assist the source in delivering the data, and second, allowing the transmission of coded packets so that multicast receivers belonging to different multicast groups can decode and extract their data concurrently. Results show that the proposed assistance paradigm reduces multicast time and increases throughput significantly.

Receiver-Based Channel Allocation for Wireless Cognitive Radio Mesh Networks

Empowered by the cognitive radio technology and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks, also referred to as cognitive radio networks, have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. In this paper, we study the channel allocation problem in wireless cognitive mesh networks. For the allocation to be feasible, served mesh clients must establish connectivity with a backbone network in both the upstream and the downstream directions, and must have the SINR (signal-to-interference and noise-ratio) of the uplink and the downlink with their parent mesh routers within a predetermined threshold. We propose a receiver-based channel allocation strategy and show that this strategy outperforms other strategies in terms of the number of mesh clients served, and the fact that no common control channel is needed for coordinating the communication process. Furthermore, we formulate the receiver-based channel allocation problem in wireless cognitive mesh network as a mixed integer linear program (MILP) and propose a heuristic solution.

Assisted-Multicast Scheduling in Wireless Cognitive Mesh Networks

In this work, we consider the multicast problem in a single cell in a cognitive mesh network. Due to the potential heterogeneity in channel availability among the members of a multicast group(s), the total multicast time could be longer due to transmitting the multicast data over multiple channels. We propose, in this work, an assisted multicast strategy with the objective of minimizing the total multicast time. This assistance is composed of two main activities, first, allowing the receivers in a multicast group to forward the data they have received to other members of the multicast group(s), and second, allowing the transmission of coded (bitwise XORed) packets so that receivers belonging to different multicast groups can decode and extract their data concurrently. We show, in this paper, that the proposed assistance paradigm achieves a considerable reduction in the total multicast time, which in turn increases the system throughput.

Scalable and Dynamic Global Power Management for Multicore Chips

The design for continuous computer performance is increasingly becoming limited by the exponential increase in the power consumption. In order to improve the energy efficiency of multicore chips, we propose a novel global power management technique. The goal of the technique is to deliver the maximum performance at a fixed power budget, without significant overhead. To tackle the exponential complexity of the power management for multiple cores, we apply a Reinforcement Learning technique, Q-learning, at the core level and then use a chip-level intelligent controller to optimize the power distribution among all cores. The power assignment adapts dynamically at runtime depending on the needs of the applications. The technique was evaluated using the PARSEC benchmark suite on a full system simulator. The experimental results show, in average, that with the proposed technique the overall performance is increased by 39% for a fixed power budget while the EDP is improved by 28%, compared to the non-DVFS baseline implementation.

Receiver-based channel allocation in cognitive radio wireless mesh networks

In this paper, we study the channel allocation problem in cognitive radio wireless mesh networks (CR-WMNs). We aim at finding an allocation strategy that guarantees quality of service (QoS) (link reliability), maximizes network coverage, and alleviates the need for a common control channel to coordinate the communication process. The allocation of a particular channel to a mesh client (MC) is considered feasible if the MC can establish connectivity with the backbone network in both the upstream and the downstream directions, and has the signal-to-interference-plus-noise ratio (SINR) of the uplink and the downlink with its parent mesh router (MR) within a predetermined threshold. A receiver-based channel allocation (RBA) model that achieves the aforementioned objectives is proposed (channel assignment under this model can be proven to be NP-hard). We then formulate a mixed integer linear program, of the channel allocation problem under the proposed model, and compare its performance to that of two other baseline models, namely, transmitter-based and all-tunable channel allocation strategies. The results prove the superiority of the proposed model. We also developed a heuristic algorithm, which is shown to be an accurate algorithm.

Real-Time Statistical Modeling of Blood Sugar

Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient’s historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.

E-Smart Real-Time Blood Sugar Administration

We develop a prototype for real-time blood sugar control based on the hypothesis that there is a medical challenge in determining the exact, real-time insulin dose. Our system controls blood sugar by observing the blood sugar level and automatically determining the appropriate insulin dose based on patient’s historical data, all in real time. At the heart of our system is an algorithm that determines the appropriate insulin dose. Our algorithm consists of two phases. In the first phase, the algorithm identifies the insulin dose offline using a Markov Process based model. In the other phase, it recursively trains the web hosted Markov model to adapt to different human bodies responsiveness.