• Energy Research

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Maintaining critical data access latency requirements and ensuring efficient energy consumption on the field devices are two important challenges of Industry 4.0. The traditional, centralized industrial networks which offer primitive data distribution functions, might be incapable of meeting such strict requirements. In this paper, in order to overcome this issue, we focus on a network of resource constraint IoT devices, and exploit the presence of a few more capable Edge nodes that act as distributed local data storing proxies for all IoT devices. We show that, given the proxy locations in the network, the initial energy supplies of the nodes, a pattern of data requests from IoT devices, and the maximum access latency that consumer nodes can tolerate, the problem of finding how to distribute the data on the Edge nodes maximizing the network lifetime is computationally hard. We design an offline centralized heuristic algorithm for identifying which paths in the network the data should follow and on which proxies they should be cached, in order to meet the data access latency constraint and to maximize the network lifetime. We implement the method and evaluate its performance using a testbed of IEEE 802.15.4-enabled network nodes. We demonstrate that the proposed heuristic (i) guarantees data access latency below the given threshold, and (ii) performs well in terms of network lifetime with respect to a theoretically optimal solution.

Peer-to-peer wireless power transfer (P2P-WPT) enables portable devices to mutually exchange energy. In opportunistic mobile networks, P2P-WPT can be uncertain due to the varying user inter-meeting duration. Existing P2P-WPT methods (unrealistically) assume the users to be exchanging energy at each opportunity, to be able to interact with all users, or the inter-node meeting duration to be unaffected by users' mobility. In this paper, in contrast to the state-of-the-art, not only we constitute more fine-grained, realistic assumptions for P2PWPT, but also we design MobiWEB, a mobility-aware energy balancing method, which employs (for the first time) a predictor for estimating the mobility information of users. MobiWEB selects the different pairs of peers for energy exchange, such that the network energy distribution is balanced while minimizing the loss and energy difference between the peers. MobiWEB, when compared to the state-of-the-art, achieves different performance trade-offs between energy balance quality, convergence time, and energy-efficiency.

Wireless energy transfer is an emerging technology that is used in networks of battery-powered devices in order to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology on networks of relatively strong computational and communicational capabilities (wireless sensor networks, ad-hoc networks); also they assume energy transfer from special chargers to regular network nodes. Different from these works, we study how to efficiently transfer energy wirelessly in populations of battery-limited devices, towards prolonging their lifetime. In contrast to the state-of-the-art, we assume a much weaker population of distributed devices which are exchanging energy in a "peer to peer" manner with each other, without any special charger nodes. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively, under both loss-less and lossy power transfer assumptions. Three protocols are designed, analyzed and evaluated, achieving different performance trade-offs between energy balance quality, convergence time and energy efficiency.

Rapid technological advances in the domain of Wireless Power Transfer pave the way for novel methods for power management in systems of wireless devices, and recent research works have already started considering algorithmic solutions for tackling emerging problems. In this paper, we investigate the problem of efficient and balanced Wireless Power Transfer in Wireless Sensor Networks. We employ wireless chargers that replenish the energy of network nodes. We propose two protocols that configure the activity of the chargers. One protocol performs wireless charging focused on the charging efficiency, while the other aims at proper balance of the chargers’ residual energy. We conduct detailed experiments using real devices and we validate the experimental results via larger scale simulations. We observe that, in both the experimental evaluation and the evaluation through detailed simulations, both protocols achieve their main goals. The Charging Oriented protocol achieves good charging efficiency throughout the experiment, while the Energy Balancing protocol achieves a uniform distribution of energy within the chargers.