An efficient scalable scheduling mac protocol for underwater sensor networks

Underwater Sensor Networks (UWSNs) utilise acoustic waves with comparatively lower loss and longer range than those of electromagnetic waves. However, energy remains a challenging issue in addition to long latency, high bit error rate, and limited bandwidth. Thus, collision and retransmission should be efficiently handled at Medium Access Control (MAC) layer in order to reduce the energy cost and also to improve the throughput and fairness across the network. In this paper, we propose a new reservation-based distributed MAC protocol called ED-MAC, which employs a duty cycle mechanism to address the spatial-temporal uncertainty and the hidden node problem to effectively avoid collisions and retransmissions. ED-MAC is a conflict-free protocol, where each sensor schedules itself independently using local information. Hence, ED-MAC can guarantee conflict-free transmissions and receptions of data packets. Compared with other conflict-free MAC protocols, ED-MAC is distributed and more reliable, i.e., it schedules according to the priority of sensor nodes which based on their depth in the network. We then evaluate design choices and protocol performance through extensive simulation to study the load effects and network scalability in each protocol. The results show that ED-MAC outperforms the contention-based MAC protocols and achieves a significant improvement in terms of successful delivery ratio, throughput, energy consumption, and fairness under varying offered traffic and number of nodes

Moving big data to the cloud

Mini-Conference - IEEE INFOCOM 2013Cloud computing, rapidly emerging as a new computation paradigm, provides agile and scalable resource access in a utility-like fashion, especially for the processing of big data. An important open issue here is how to efficiently move the data, from different geographical locations over time, into a cloud for effective processing. The de facto approach of hard drive shipping is not flexible, nor secure. This work studies timely, cost-minimizing upload of massive, dynamically-generated, geo-dispersed data into the cloud, for processing using a MapReduce-like framework. Targeting at a cloud encompassing disparate data centers, we model a cost-minimizing data migration problem, and propose two online algorithms, for optimizing at any given time the choice of the data center for data aggregation and processing, as well as the routes for transmitting data there. The first is an online lazy migration (OLM) algorithm achieving a competitive ratio of as low as 2.55, under typical system settings. The second is a randomized fixed horizon control (RFHC) algorithm achieving a competitive ratio of 1+ 1/l+1 κ/λ with a lookahead window of l, where κ and λ are system parameters of similar magnitude. © 2013 IEEE.published_or_final_versio

Adaptive Street Lighting Predictive Control

Abstract In this paper an implementation of a smart predictive monitoring and adaptive control system for the public lighting have been carried out. The vehicular traffic flow acquired using a smart camera has been analyzed and several predictive methods have been studied. Then, a control strategy based on the given traffic forecasts and on the dynamical street class downgrade allowed by the law, has been implemented. Experimental results provided by a real life testbed showed that the proposed strategy has high potential energy savings without affecting safety