Nor Jaidi Tuah

Resource-aware speculative prefetching in wireless networks

Mobile users connected to wireless networks expect performance comparable to those on wired networks for interactive multimedia applications. Satisfying Quality of Service (QoS) requirements for such applications in wireless networks is a challenging problem due to limitations of low bandwidth, high error rate and frequent disconnections of wireless channels. In addition, wireless networks suffer from varying bandwidth. In this paper we investigate object prefetching during times of connectedness and bandwidth availability to enhance user perceived connectedness. This paper presents an access model that is suitable for multimedia access in wireless networks. Access modelling for the purpose of predicting future accesses in the context of speculative prefetching has received much attention in the literature. The model recognizes that a web page, instead of just a single file, is typically a compound of several files. When it comes to making prefetch decisions, most previous studies in speculative prefetching resort to simple heuristics, such as prefetching an item with access probabilities larger than a manually tuned threshold. This paper takes a different approach. Specifically, it models the performance of the prefetcher, taking into account access predictions and resource parameters, and develops a prefetch policy based on a theoretical analysis of the model. Since the analysis considers cache as one of the resource parameters, the resulting policy integrates prefetch and cache replacement decisions. The paper investigates the effect of prefetching on network load. In order to make effective use of available resources and maximize access improvement, it is beneficial to prefetch all items with access probabilities exceeding certain threshold.

Performance optimization problem in speculative prefetching

Speculative prefetching has been proposed to improve the response time of network access. Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modeling. We analyze the performance of a prefetcher that has uncertain knowledge about future accesses. Our performance metric is the improvement in access time, for which we derive a formula in terms of resource parameters (time available and time required for prefetching) and speculative parameters (probabilities for next access). We develop a prefetch algorithm to maximize the improvement in access time. The algorithm is based on finding the best solution to a stretch knapsack problem, using theoretically proven apparatus to reduce the search space. An integration between speculative prefetching and caching is also investigated.

Communication-Aware Route Selection in Wireless Sensor Networks

We consider the problem of optimal route selection for wireless sensor network in the presence of path loss, multipath fading, interference, and environmental noise. The communication-aware route selection strategy is proposed by incorporating realistic communication model portraying the underlying dynamics of wireless link. The link quality is characterized by the probability of successfully received packets over a communication link, so-called reception probability. We utilize reception probability as a metric for communication quality-oriented route selection and to compare its performance with the conventional metrics, i.e., Hop count and Euclidean distance. The simulation results demonstrate that reception probability-based route selection provides optimal end-to-end throughput in wireless sensor networks.

Effect of speculative prefetching on network load in distributed systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper on the other hand investigates the performance of speculative prefetching. When prefetching is performed speculatively, there is bound to be an increase in the network load. Furthermore, the prefetched items must compete for space with existing cache occupants. These two factors-increased load and eviction of potentially useful cache entries-are considered in the analysis. We obtain the following conclusion: to maximise the improvement in access time, prefetch exclusively all items with access probabilities exceeding a certain threshold.

Fuzzy based room temperature control by integrating sensors and cameras with a grid

With energy consumption on the rise, many researches have focused on the process of monitoring resources to ensure energy efficiency. Monitoring indoor environment is vital to control the use of various high consumption rate devices like air-conditioners lights, etc. Based on the brightness of the room one could decide whether to switch on or off the lights. Integrating two complementing technologies in sensor and grid could provide the required facilities for many applications. Low-cost., multimedia sensors connected to distributed decision-making and acting systems supersede the centrally controlled power management systems. In this work, the indoor environment is sensed using sensors (that sense temperature and humidity) and cameras. In this paper, information is synchronized and processed using a wired grid, along with a fuzzy system. The outcome is used to infer the current state of the system and to perform appropriate actions to make the indoor environment conducive for working. Fusion of information from different sources helps to nullify any erroneous information that is received from the sensors or cameras.

Neuromuscular Fatigue Analysis of Soldiers Using DWT Based EMG and EEG Data Fusion During Load Carriage

This research reports peripheral and central fatigue of soldiers during load carriage on a treadmill. Electromyography (EMG) was used to investigate peripheral fatigue of lower extremity muscles and electroencephalography (EEG) was used for central fatigue detection on frontal lobe of the brain. EMG data were processed using Db5 and Rbio3.1 discrete wavelet transforms with a six levels of decomposition and EEG data were iteratively transformed into multi-resolution subsets of coefficients using Db8 wavelet function to perform the power spectrum analysis of alpha, beta and theta waves. Peak alpha frequency (PAF) was also calculated for EEG signals. A majority of significant results (p < 0.05) from EMG signals were observed in the lower extremity muscles using Db5 wavelet function at all conditions. While, significant changes were only observed during unloaded conditions at the frontal cortex. Significant changes (p < 0.05) in the PAF was also detected at certain conditions in the pre-frontal and frontal cortex. A significant increase in heart rate and rating of perceived exertion values were seen at all conditions. Hence, peripheral fatigue was the cause of the exhaustion sustained by the soldiers during load carriage which sends signals to the brain for decision making as to stop the exercise.

Coordinated communication and position aware route reconfiguration for throughput optimisation in mobile sensor networks

In this paper, we demonstrate the improvement in end-to-end throughput by coordinated reconfiguration of the route using the information exchanged between the routers. A coordination strategy of route reconfiguration is developed by integrating communication quality, particle swarm optimisation (PSO), and mobility control of multiple routers in the presence of path loss, noise, multipath Rayleigh fading, and interference. The route with the highest throughput is determined using communication quality oriented routing metric prior to the route reconfiguration. We then show that how a coordinator can identify the router with critical link and coordinate the actions of multiple routers to gradually steer them towards a reconfigured route with higher end-to-end throughput. The simulation results illustrate that the proposed strategy provide routes with better performance in terms of throughput as compared to the routes generated by the non-coordinated route reconfiguration framework.

Fuzzy Logic and Dempster-Shafer belief theory to detect the risk of disease spreading of African Trypanosomiasis

This research presents the risk of disease spreading of African Trypanosomiasis using Fuzzy logic and Dempster-Shafer belief theory. Integrating Fuzzy Logic and Dempster-Shafer belief theory by calculating the similarity between Fuzzy membership function in the context to detect the risk of disease spreading of African Trypanosomiasis and finally to develop a realistic and useful Web mapping for displaying maps on a screen to locate the risk of African Trypanosomiasis spreading. The risk of African Trypanosomiasis spreading is not classified according to higher population density which is equal to higher risk. This research has considered population changes in an area to detect the risk of African Trypanosomiasis spreading. The experts provide opinion in terms of basic probability assignment for interval or crisp focal elements. Those basic probability assignments are proposed based on the similarity measure between membership function. The similarity between Fuzzy membership function is calculated to get basic probability assignment. The highest percentage of the risk of disease spreading of African Trypanosomiasis is 17%. In Angola and Zambia, the highest basic probability assignment of the risk of disease spreading of African Trypanosomiasis is very low which is equal to 0.173. The risk of disease spreading of African Trypanosomiasis in areas which include Angola, Botswana, Congo, Congo DRC, Malawi, Mozambique, Namibia, Tanzania, Zambia, and Zimbabwe.

PSO Based Throughput Optimization for Mobile Sensor Networks

This paper addresses the problem of optimal route selection and throughput optimization using heuristic computation in realistic communication environments for mobile sensor networks. The strategy is developed in two phases: communication aware optimization that selects the optimal route by using communication quality as the metric for routing decision; and position aware optimization that identifies the bottleneck link on the selected route, then enables route reconfiguration by relocating the bottleneck involved relay to the optimal position with higher throughput. The optimal position is found using particle swarm optimization and mobility of the relay is governed by a feedback control. We demonstrate through simulations that proposed strategy provides better routes in terms of end-to-end throughput as compared to other routing strategies based on ideal communication models.