Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A method, apparatus and computer program product for -based, delay-efficient data transmission for broadcasting a single file is presented. A file (f) comprised of K packets to be broadcast to a plurality of receivers is determined. A plurality of packets (Pk) of the file are selected for transmission during a timeslot (t). Next, a linear combination of the selected packets (P[t]) are produced, the linear combination of packets are selected at random within the file. The linear combination of selected packets is then transmitted to a plurality of receivers over unreliable channels.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A system and method for allocating resources in a network is disclosed. The system and method comprises a proactive resource allocation framework in which the predictability of user behavior is exploited to balance the network traffic over time and to reduce the bandwidth required to achieve a given blocking/outage probability. The disclosed proactive resource allocation framework avoids limitations associated with off-peak demand and achieves a significant reduction in the peak to average demand ratio without relying on out of network users. It is based on a model in which smart devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, the resulting prediction diversity gain is quantified to establish that the decay rate of the outage event probabilities increases linearly with the prediction duration T.

Abstract: A method, apparatus and computer program product for -based, delay-efficient data transmission for broadcasting a single file is presented. A file (f) comprised of K packets to be broadcast to a plurality of receivers is determined. A plurality of packets (Pk) of the file are selected for transmission during a timeslot (t). Next, a linear combination of the selected packets (P[t]) are produced, the linear combination of packets are selected at random within the file. The linear combination of selected packets is then transmitted to a plurality of receivers over unreliable channels.

Abstract: A method, apparatus and computer program product for -based, delay-efficient data transmission for broadcasting a single file is presented. A file (ƒ) comprised of K packets to be broadcast to a plurality of receivers is determined. A plurality of packets (Pk) of the file are selected for transmission during a timeslot (t). Next, a linear combination of the selected packets (P[t]) are produced, the linear combination of packets are selected at random within the file. The linear combination of selected packets is then transmitted to a plurality of receivers over unreliable channels.

Abstract: A proactive networking system and method is disclosed. The network anticipates the user demands in advance and utilizes this predictive ability to reduce the peak to average ratio of the wireless traffic and yield significant savings in the required resources to guarantee certain Quality of Service (QoS) metrics. The system and method focuses on the existing cellular architecture and involves the design and analysis of learning algorithms, predictive resource allocation strategies, and incentive techniques to maximize the efficiency of proactive cellular networks. The system and method further involve proactive peer-to-peer (P2P) overlaying, which leverages the spatial and social structure of the network. Machine learning techniques are applied to find the optimal tradeoff between predictions that result in content being retrieved that the user ultimately never requests, and requests that are not anticipated in a timely manner.

Abstract: A system and method for allocating resources in a network is disclosed. The system and method comprises a proactive resource allocation framework in which the predictability of user behavior is exploited to balance the network traffic over time and to reduce the bandwidth required to achieve a given blocking/outage probability. The disclosed proactive resource allocation framework avoids limitations associated with off-peak demand and achieves a significant reduction in the peak to average demand ratio without relying on out of network users. It is based on a model in which smart devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, the resulting prediction diversity gain is quantified to establish that the decay rate of the outage event probabilities increases linearly with the prediction duration T.