We consider the problem of data collection from a continental-scale network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favour a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimisation problem for energy harvesting mobile sensor network, that maximises the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-the-art. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.
It is well known that max-weight policies based on a queue backlog index can be used to stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using Lyapunov Optimization, we extend this analysis to design a utility maximizing algorithm that uses explicit delay information from the head-of-line packet at each user. The resulting policy is shown to ensure deterministic worst-case delay guarantees, and to yield a throughput-utility that differs from the optimally fair value by an amount that is inversely proportional to the delay guarantee. Our results hold for a general class of 1-hop networks, including packet switches and multi-user wireless systems with time varying reliability. Body Sensor Networks (BSNs) consist of miniature sensors deployed on or implanted into the human body for health monitoring. Conserving the energy of these sensors, while guaranteeing a required level of performance, is a key challenge in BSNs. In terms of communication protocols, this translates to minimizing energy consumption while limiting the latency in data transfer. In this paper, we focus on polling-based communication protocols for BSNs, and address the problem of optimizing the polling schedule to achieve minimal energy consumption and latency. We show that this problem can be posed as a geometric program, which belongs to the class of convex optimization problems, solvable in polynomial time. We also introduce a dynamic priority vector for each sensor, based on the observation that relative priorities of sensors in a BSN change over time. This vector is used to develop a decision-tree based approach for resolving scheduling conflicts among devices. The proposed framework is applicable to a broad class of periodic polling-based communication protocols. We design one such protocol in detail and show that it achieves an improvement of approximately 45 over the widely accepted standard IEEE 802.15.4 MAC protocol. We first consider a multi-user, single-hop wireless network with arbitrarily varying (and possibly non-ergodic) arrivals and channels. We design an opportunistic scheduling algorithm that guarantees all sessions have a bounded worst case delay. The algorithm has no knowledge of the future, but yields throughput-utility that is close to (or better than) that of a T-slot lookahead policy that makes “ideal” decisions based on perfect knowledge up to T slots into the future. We then extend the algorithm to treat worst case delay guarantees in multi-hop networks. Our analysis uses a sample-path version of Lyapunov optimization together with a novel virtual queue structure. Wireless Sensor Networks (WSN) are often densely deployed in the region of interest in order to continuously monitor physical phenomenon. Due to highly deployment density and the nature of the physical phenomenon, nearby sensor readings are often highly correlated in both space domain and time domain. These spatial and temporal correlations bring significant potential advantages as well as challenges for developing efficient sensing scheduling protocols for WSN. In this paper, a theoretical framework is developed to model the Quality of Monitoring (QoM) by exploiting both spatial and temporal correlations. The objective of this work is to enable the development of efficient sensing scheduling protocols which exploit these advantageous intrinsic features of the WSN paradigm. Specially, we propose two sensing scheduling schemes in order to maximize the overall QoM subject to resource constraints (e.g., under fixed duty cycle). Extensive experiments validate our theoretical results.