Towards offering more useful data reliably to mobile cloudfrom wireless sensor network

The integration of ubiquitous wireless sensor network (WSN) and powerful mobile cloud computing (MCC) is a research topic that is attracting growing interest in both academia and industry. In this new paradigm, WSN provides data to the cloud, and mobile users request data from the cloud. To support applications involving WSN-MCC integration, which need to reliably offer data that are more useful to the mobile users from WSN to cloud, this paper first identifies the critical issues that affect the usefulness of sensory data and the reliability of WSN, then proposes a novel WSN-MCC integration scheme named TPSS, which consists of two main parts: 1) TPSDT (Time and Priority based Selective Data Transmission) for WSN gateway to selectively transmit sensory data that are more useful to the cloud, considering the time and priority features of the data requested by the mobile user; 2) PSS (Priority-based Sleep Scheduling) algorithm for WSN to save energy consumption so that it can gather and transmit data in a more reliable way. Analytical and experimental results demonstrate the effectiveness of TPSS in improving usefulness of sensory data and reliability of WSN for WSN-MCC integration

Rapid Convergecast on Commodity Hardware: Performance Limits and Optimal Policies

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Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks

In this paper, we consider an intrusion detection application for Wireless Sensor Networks (WSNs). We study the problem of scheduling the sleep times of the individual sensors to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous state-action spaces, in a manner similar to (Fuemmeler and Veeravalli [2008]). However, unlike their formulation, we consider infinite horizon discounted and average cost objectives as performance criteria. For each criterion, we propose a convergent on-policy Q-learning algorithm that operates on two timescales, while employing function approximation to handle the curse of dimensionality associated with the underlying POMDP. Our proposed algorithm incorporates a policy gradient update using a one-simulation simultaneous perturbation stochastic approximation (SPSA) estimate on the faster timescale, while the Q-value parameter (arising from a linear function approximation for the Q-values) is updated in an on-policy temporal difference (TD) algorithm-like fashion on the slower timescale. The feature selection scheme employed in each of our algorithms manages the energy and tracking components in a manner that assists the search for the optimal sleep-scheduling policy. For the sake of comparison, in both discounted and average settings, we also develop a function approximation analogue of the Q-learning algorithm. This algorithm, unlike the two-timescale variant, does not possess theoretical convergence guarantees. Finally, we also adapt our algorithms to include a stochastic iterative estimation scheme for the intruder's mobility model. Our simulation results on a 2-dimensional network setting suggest that our algorithms result in better tracking accuracy at the cost of only a few additional sensors, in comparison to a recent prior work

Energy-Delay Tradeoff and Dynamic Sleep Switching for Bluetooth-Like Body-Area Sensor Networks

Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people's health. This paper considers a system scenario relevant to such applications, where a smart-phone acts as a data-collecting hub, gathering data from a number of wireless-capable body sensors, and relaying them to a healthcare provider host through standard existing cellular networks. Delay of critical data and sensors' energy efficiency are both relevant and conflicting issues. Therefore, it is important to operate the wireless body-area sensor network at some desired point close to the optimal energy-delay tradeoff curve. This tradeoff curve is a function of the employed physical-layer protocol: in particular, it depends on the multiple-access scheme and on the coding and modulation schemes available. In this work, we consider a protocol closely inspired by the widely-used Bluetooth standard. First, we consider the calculation of the minimum energy function, i.e., the minimum sum energy per symbol that guarantees the stability of all transmission queues in the network. Then, we apply the general theory developed by Neely to develop a dynamic scheduling policy that approaches the optimal energy-delay tradeoff for the network at hand. Finally, we examine the queue dynamics and propose a novel policy that adaptively switches between connected and disconnected (sleeping) modes. We demonstrate that the proposed policy can achieve significant gains in the realistic case where the control "NULL" packets necessary to maintain the connection alive, have a non-zero energy cost, and the data arrival statistics corresponding to the sensed physical process are bursty.Comment: Extended version (with proofs details in the Appendix) of a paper accepted for publication on the IEEE Transactions on Communication

A Cross-Layer Approach for Minimizing Interference and Latency of Medium Access in Wireless Sensor Networks

In low power wireless sensor networks, MAC protocols usually employ periodic sleep/wake schedule to reduce idle listening time. Even though this mechanism is simple and efficient, it results in high end-to-end latency and low throughput. On the other hand, the previously proposed CSMA/CA-based MAC protocols have tried to reduce inter-node interference at the cost of increased latency and lower network capacity. In this paper we propose IAMAC, a CSMA/CA sleep/wake MAC protocol that minimizes inter-node interference, while also reduces per-hop delay through cross-layer interactions with the network layer. Furthermore, we show that IAMAC can be integrated into the SP architecture to perform its inter-layer interactions. Through simulation, we have extensively evaluated the performance of IAMAC in terms of different performance metrics. Simulation results confirm that IAMAC reduces energy consumption per node and leads to higher network lifetime compared to S-MAC and Adaptive S-MAC, while it also provides lower latency than S-MAC. Throughout our evaluations we have considered IAMAC in conjunction with two error recovery methods, i.e., ARQ and Seda. It is shown that using Seda as the error recovery mechanism of IAMAC results in higher throughput and lifetime compared to ARQ.Comment: 17 pages, 16 figure