In Ambient Assistant Living(AAL) systems, it is a fundamental problem to ensure prompt delivery of detected events, such as irregular heart rate or fall of elderly, to a central processing device(e.g. gateway node). M...In Ambient Assistant Living(AAL) systems, it is a fundamental problem to ensure prompt delivery of detected events, such as irregular heart rate or fall of elderly, to a central processing device(e.g. gateway node). Most of recently proposed MAC protocols for low-power embedded sensing systems(e.g. wireless sensor networks) are designed with energy efficiency as the first goal, so they are not suitable for AAL systems. Although some multi-channel MAC protocols have been proposed to address the problem, most of those protocols ignore the cost of channel switching, which can have reverse effect on network performance, especially latency of data delivery. In this paper, we propose a Delay-Sensitive Multi-channel MAC protocol(DS-MMAC) for AAL systems, which can provide high packet delivery ratio and bound low latency for data delivered to the gateway node. The novelty of the protocol is that an efficient distributed time slot scheduling and channel assignment algorithm is combined with the process of route establishment, which takes the channel switching cost into account and reduces endto-end delay to meet the required delay bound of each data flow. The performance of the proposed protocol is evaluated through extensive simulations. Results show that DS-MMAC can bound low latency for delivering detected events in AAL system to the gateway, while providing high delivery reliability and low energy consumption.展开更多
Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation,communication,storage,and analy...Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation,communication,storage,and analytics closer to the End Users(EUs).In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things(IoT)applications,fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources.In this paper,we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies.The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control(MAC)-layer scheduling among sensor devices.In the second case study on task offloading,we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby,at the same time,minimize its own energy and latency objectives.Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing.展开更多
基金supported by the International S&T Cooperation Program of China (ISTCP) under Grant No. 2013DFA10690the National Science Foundation of China (NSFC) under Grant No. 61100180
文摘In Ambient Assistant Living(AAL) systems, it is a fundamental problem to ensure prompt delivery of detected events, such as irregular heart rate or fall of elderly, to a central processing device(e.g. gateway node). Most of recently proposed MAC protocols for low-power embedded sensing systems(e.g. wireless sensor networks) are designed with energy efficiency as the first goal, so they are not suitable for AAL systems. Although some multi-channel MAC protocols have been proposed to address the problem, most of those protocols ignore the cost of channel switching, which can have reverse effect on network performance, especially latency of data delivery. In this paper, we propose a Delay-Sensitive Multi-channel MAC protocol(DS-MMAC) for AAL systems, which can provide high packet delivery ratio and bound low latency for data delivered to the gateway node. The novelty of the protocol is that an efficient distributed time slot scheduling and channel assignment algorithm is combined with the process of route establishment, which takes the channel switching cost into account and reduces endto-end delay to meet the required delay bound of each data flow. The performance of the proposed protocol is evaluated through extensive simulations. Results show that DS-MMAC can bound low latency for delivering detected events in AAL system to the gateway, while providing high delivery reliability and low energy consumption.
文摘Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation,communication,storage,and analytics closer to the End Users(EUs).In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things(IoT)applications,fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources.In this paper,we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies.The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control(MAC)-layer scheduling among sensor devices.In the second case study on task offloading,we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby,at the same time,minimize its own energy and latency objectives.Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing.
