In Chapter 3 and Chapter 4 the problem of designing Framed-ALOHA based MAC protocols for single-hop EH networks is investigated. The considered application is a batch resolution problem [36], where data packets are periodically generated at the nodes and need to be collected by a central fusion center in a star-topology network. The EH arrivals at the nodes are described by an arbitrary probability distribution and the energy storage devices are assumed to be finite, while the communication links are subject to random fading.
To assess the novel trade-offs in the design of MAC protocols for EH networks, Chapter 3 proposes to utilize two performance metrics. The first metric, referred to as time efficiency, measures the data collection rate at the fusion center, while the second metric, referred to as delivery probability, accounts for the probability that any packet generated at the nodes is eventually collected by the fusion center. Due to the potential perpetual operations of the nodes enabled by EH, the proposed performance metrics are assessed over a long-term period by developing a mathematical framework based on Markov models, which describes the evolution of the energy availability
at the nodes along time. The critical issue in ALOHA-based scheme of estimating the number of nodes involved in the transmission in each frame is also tackled by proposing a practical reduced-complexity algorithm. This scheme is an extension of the one proposed in [13] that is designed to account for the EH nature of the nodes. From the analysis of the performance metrics described above, it is inferred that the trade-off between time efficiency and delivery probability is dramatically affected by a design parameter that is used to select the frame size in the framed- ALOHA protocol, which in turns depends on the number of transmitting nodes in each frame. It is shown that the choice of such parameter strongly depends on the probability distribution of the EH processes and on the desired trade-off between time efficiency and delivery probability. Based on this insight, a new protocol, referred to as energy group dynamic framed-ALOHA (EG-DFA), is proposed in Chapter 4. The proposed EG-DFA protocol creates groups of nodes according to their energy availability and runs optimized and separated instances of the DFA protocol for each group. It is shown that by judiciously choosing the frame-size parameter for each group of nodes the EG-DFA protocol can remarkably outperform the conventional DFA protocol.
The work in these chapters is based on:
• F. Iannello, O. Simeone, and U. Spagnolini, “Medium access control protocols for wireless sensor networks with energy harvesting,” IEEE Trans. Commun., May 2012 (in press).
• F. Iannello, O. Simeone, P. Popovski and U. Spagnolini, “Energy group-based dynamic framed ALOHA for wireless networks with energy harvesting,” in Proc. 46th Conf. Inf. Sci. Syst. (CISS ), Princeton, NJ, Mar. 2012.
• F. Iannello, O. Simeone, and U. Spagnolini, “Dynamic framed-ALOHA for energy-constrained wireless sensor networks with energy harvesting,” in Proc. IEEE GLOBECOM, Miami, USA, Dec. 2010.
1.6.3 Centralized Scheduling MAC Protocols
The third important aspect considered in this dissertation is the design of scheduling- based MAC protocols for EH networks. This issue is addressed in Chapter 5 and Chapter 6. As anticipated in Section 1.5.3, few previous works considered scheduling problems in EH networks. In particular [29] consider a two-nodes system with deterministic energy arrivals, while [28] considers a generally suboptimal Lyapunov optimization approach for a scheduling problems in arbitrarily interconnected networks.
In this dissertation the focus is instead on a star-topology network in which a central fusion center collects data packets that are generated periodically by a set of M nodes, similar to the model considered in Section 1.6.2. The nodes harvest energy from the environment, and their energy storage devices are finite and possibly subject to energy leakage. In each data collection period only a subset of K ≤ M nodes is given the chance of transmitting over orthogonal transmission resources, which are allocated by the fusion center.
As mentioned in the previous sections, since the activity of most EH sources is uncertain and unpredictable, nodes that are exclusively powered via EH are possibly subject to temporary energy shortages. Based on this observation, it is possible to distinguish two different scenarios: i ) Applications that require continuous operation of the nodes and that do not tolerate temporary energy shortages; ii ) applications that tolerate energy shortages. When applications do not tolerate energy shortages, it is not uncommon that EH is used as a secondary energy source that complements the use of a non-rechargeable battery [37]. In this case the nodes are equipped with a so
called hybrid energy storage system (HESS), which is composed by a non-rechargeable battery and, e.g., a capacitor that stores the energy harvested from the environment. The network design goal here is to maximize the lifetime of the non-rechargeable batteries. When applications that tolerate temporary energy shortages are instead considered, EH can be used as the unique energy source, and the scheduling policies are designed so as to maximize the network throughput. Scheduling problems for both scenarios are addressed in Chapter 5 and Chapter 6.
In particular, optimal scheduling policies that maximize the battery lifetime of the HESS-nodes are derived under the assumptions that: the fusion center has perfect and instantaneous knowledge of the energy availability at the nodes; the nodes are subject to either energy harvesting only or energy leakage only; the energy harvesting and energy leakage are described by binary random processes, which are assumed symmetric and independent at the nodes and over time. The general case when both energy harvesting and energy leakage processes are non-negligible still remains an open problem.
The scheduling problems above are then addressed under the assumption that the fusion center does not have instantaneous information of the energy availability at the nodes. In this case, the only information available at the fusion center is given by the knowledge of the statistical properties of the energy harvesting and leakage processes at the nodes and by the (observable) history of the system state. The scheduling problem is then formulated as a partially observable Markov decision process (POMDP), which can be seen a restless multiarmed bandit (RMAB) problem [38]. In the scenario in which nodes are equipped with HESS, finding optimal policies explicitly is not straightforward, and thus only heuristic policies are proposed and compared to the full state information scenario.
For the scenario in which the nodes are powered exclusively via EH and under partial state information at the fusion center, optimal scheduling policies are derived
under the assumption that the ESD at the nodes is of capacity one. For this case, it is shown that a myopic, or greedy, policy that operates on the space of the a posteriori probabilities (or beliefs) of the nodes energy levels is optimal. Moreover, it is demonstrated that such policy coincides with the so called Whittle index policy. It is worth mentioning that the derivation of the optimality of the myopic policy and of the Whittle index policy is related to complementary findings in RMAB problems arising in cognitive radio applications [39, 40]. Finally, when the size of the capacitors are arbitrary, a performance upper bound is derived and compared with the performance of the generally suboptimal myopic policy.
The work in these chapters is based on:
• F. Iannello, O. Simeone and U. Spagnolini, “Lifetime maximization for wireless networks with hybrid energy storage systems,” in preparation for submission to IEEE Trans. Commun.
• F. Iannello, O. Simeone and U. Spagnolini, “On the optimal scheduling of independent, symmetric, and time-sensitive tasks,” submitted to IEEE Trans. Autom. Control (under first revision).
• F. Iannello, O. Simeone and U. Spagnolini, “Optimality of myopic scheduling and whittle indexability for energy harvesting sensors,” in Proc. 46th Conf. Inf. Sci. Syst. (CISS ), Princeton, NJ, Mar. 2012.
Energy Management Policies for Single-node Systems
This part of the dissertation considers a wireless network in which a single node communicates with a central station, where the latter coordinates the node’s transmissions. The node is equipped with energy harvesting (EH) and storage capabilities, so that the use of the harvested energy can be postponed over time. In general, in single node EH networks the design issue is how to trade the energy harvested from the environment with the energy needed by the node to perform the required operations, such as data transmission. Energy management policies are then designed with the aim of optimizing a given performance criterion.
A specific instance of such single node EH networks is considered in the next chapter, where a RFID system operated by enhanced RFID tags is investigated. In particular, in such system, EH is leveraged with the aim of improving the communication reliability between the tag and the central station (or RFID reader). This is done by introducing an additional power amplifier at the tag that is exclusively powered via EH. Energy scheduling policies for the power amplifier are then designed by parsimoniously trading the energy available in the tag’s energy storage device and the statistical properties of the EH process.
ENERGY MANAGEMENT POLICIES FOR ENHANCED PASSIVE RFID TAGS WITH ENERGY HARVESTING