Medhat H. Awadalla

A Data Gathering Algorithm for a Mobile Sink in Large-Scale Sensor Networks

Sink mobility is one of the most comprehensive trends for information gathering in sensor networks. This way of information gathering has a prominent role in balancing the energy consumption among sensor networks, and culling the hotspots problem of sensor networks. In this paper, a well planned adaptive moving strategy for a mobile sink in large-scale, hierarchical sensor networks is presented. The mobile sink traverses the entire network uploading the sensed data from cluster heads in time driven scenarios. The mobile sink trajectory is planned such that all heads require no multi-hop relays to reach the mobile sink. The proposed system aims at extending the lifetime of the sensor network by achieving a high level of energy efficiency and fair balancing of energy consumption across all network heads. Furthermore, reducing the loss that data incur due to buffer overflow. Extensive simulations are conducted in order to validate the proposed strategy. The adopted data gathering scheme outperforms the static sink scheme and periphery scheme in terms of life time elongation, and scalability.

Energy-efficient task allocation techniques for asymmetric multiprocessor embedded systems

Asymmetric multiprocessor systems are considered power-efficient multiprocessor architectures. Furthermore, efficient task allocation (partitioning) can achieve more energy efficiency at these asymmetric multiprocessor platforms. This article addresses the problem of energy-aware static partitioning of periodic real-time tasks on asymmetric multiprocessor (multicore) embedded systems. The article formulates the problem according to the Dynamic Voltage and Frequency Scaling (DVFS) model supported by the platform and shows that it is an NP-hard problem. Then, the article outlines optimal reference partitioning techniques for each case of DVFS model with suitable assumptions. Finally, the article proposes modifications to the traditional bin-packing techniques and designs novel techniques taking into account the DVFS model supported by the platform. All algorithms and techniques are simulated and compared. The simulation shows promising results, where the proposed techniques reduced the energy consumption by 75% compared to traditional methods when DVFS is not supported and by 50% when per-core DVFS is supported by the platform.

Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks

Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive gathering strategy resilient to both connected and disconnected networks. The proposed gathering strategy geared to eliminate multihop relays required by all cluster heads to reach the mobile sink, balancing the traffic load across all network heads, meanwhile, reducing the loss that data may incur due to buffer overflow. Furthermore, enabling the mobile sink to navigate safely within cluttered and uncluttered fields augments the proposed gathering strategy. Extensive simulations are conducted in order to validate the effectiveness of the proposed strategy. The achieved results show an improvement in overall system performance compared to other mobility strategies.

Energy-efficient multi-speed algorithm for scheduling dependent real-time tasks

Reducing energy consumption is a critical issue in the design of battery-powered embedded systems to prolong battery life. With dynamic voltage scaling (DVS) processors, energy consumption can be reduced efficiently by making appropriate decisions on the processor speed/voltage during the scheduling of real time tasks. This paper addresses the problem of energy efficient real-time task scheduling over earliest deadline first (EDF) scheduling policy where the tasks are dependent due to shared resources. Furthermore, the paper proposes enhancements over the existing multi-speed (MS) algorithm where the proposed algorithm achieves more energy saving and has the capability to function with both stack resource policy (SRP) and dynamic priority ceiling protocol (DPCP) as resource access protocols.

Arabic verb pattern extraction

Arabic is a highly inflected language, and therefore the processes of stemming and root extracting represent a challenge to researches. A new method is presented for extracting Arabic text stem, and lemma. Stemming sometimes affects the semantic of a word, where as lemma preserve the meaning of a word. The approach is based on pattern extraction. It uses a special encoding based on dividing letters into original and non-original letters. Codes are automatically generated for each pattern and then match against input text to extract root, pattern, and lemma of a word. A comparison with other methods reveals a promising result with accuracy up to 96%.

Adaptive and Energy Efficient Clustering Architecture for Dynamic Sensor Networks

Clustering is an effective topology control approach in sensor networks. This paper proposes a distributed and adaptive clustering architecture for dynamic sensor networks. The proposed architecture comprises an approach for energy-efficient clustering with adaptive node activity for achieving a good performance in terms of system lifetime and network coverage quality. This architecture demonstrates a uniform cluster head distribution across the network in addition to a desirable network coverage. Furthermore, the paper presents an analytical approach to disclose the relationship between network density and coverage quality. Experiments were conducted to validate the proposed architecture. The analytical and simulation results demonstrate that the proposed architecture prolongs network lifetime meanwhile preserving a highly coverage quality.

Energy and Synchronization-Aware Mapping of Real-Time Tasks on Asymmetric Multicore Platforms

task mapping plays a crucial role in saving energy in asymmetric multiprocessor platforms. This paper considers the problem of energy-aware static mapping of periodic real- time dependent tasks sharing resources on asymmetric multi/many-core embedded systems. The paper extends an existing synchronization-aware bin-packing (BP) variant when the full-chip dynamic voltage and frequency scaling (DVFS) is supported by the asymmetric multicore platform. Then, the paper proposes another BP variant when DVFS is not supported. The simulation results showed that the proposed BP variant can reduce energy consumption significantly in the presence of shared resources.

Multi target tracking using a compact Q-learning with a teacher

This paper focuses on developing a team of mobile robots capable of learning via human interaction. A modified Q-learning algorithm incorporating a teacher is proposed. The paper first concentrates on simplifying the Q-learning algorithm to be implemented on small and simple team of robots having limited capabilities of memory and computational power. Second it concentrates on the incorporation of a human teacher in the Q-learning algorithm. Experiments using the well-known robot simulator Webots on both single and multi-target tracking tasks have been conducted. The achieved results show the success of the proposed algorithm in the over all system performance.

Dynamic Resources for Multicore Processor using Register File Protection

massive investment in the design multicore has been accomplished through technologies that impose significant barriers to assure the reliable operation of future chips. Extremely complex, parallel, multi-core processor fabricated in these technologies will become more vulnerable to several factors that produce transient (soft) errors or permanent (hard) errors. One of the critical issues to protect a processor is the register file. It registers the architectural states for long periods and also it can be read frequently. This paper presents a new eviction policy to the registers entry from error code correction table in the insertion stage for the integer register file protection process. The paper presents a qualitative comparison with other eviction policies (random and the least recently used, LRU). Also it addresses the effect of using the integer register protection with dynamic resource fetch policy on the overall performance by adding the protection for integer registers files to the dynamic allocated resource (fetch policy). The achieved results show that the dynamic fetch policy WZ-FETCH outperforms in all addressed benchmark programs in case of using register file protection.

C28. Energy-Efficient Task Assignment on Asymmetric Multiprocessor Platforms

Despite asymmetric multiprocessor platforms are considered power-efficient multiprocessor architectures, efficient task partitioning (assignment) plays a crucial role in achieving more energy efficiency at these multiprocessor platforms. This paper considers the problem of energy-aware static partitioning of periodic real-time tasks on asymmetric multiprocessor platforms. The paper introduces a comparative evaluation of Bin-Packing (BP) techniques and variants when the Dynamic Voltage/Frequency Scaling (DVFS) is not supported by the platform and when practical full-chip DVFS with multiple Operating Performance Points (OPPs) is supported. Also, the paper introduces the energy models for each case and proposes a condition to determine which OPP will be chosen that misses no deadline and saves energy. The simulation results showed the most energy-efficient partitioning technique for each case.