Data gathering is a crucial function of wireless sensor networks (WSNs). In resource-limited WSNs, it is critical to improve cost-efficiency and prolong network lifetime. Sensor networks utilizing deterministic routing paths are particularly vulnerable to attacks. Additionally, repeated use of the same path will introduce load unbalance. In this paper, we propose a cost-aware stochastic compressive data gathering for WSNs. In contrast to traditional compressive sensing (CS) based algorithms that implicitly assume uniform transmission cost, our proposed scheme will consider the cost diversity into the CS data gathering framework. The Markov chain based model will be used to characterize the stochastic data gathering process. An optimization problem is formulated to minimize the total expected cost subjected to the constraints on global degree of randomness and the recovery error. Our proposed algorithm requires less total expected cost to achieve certain level of recovery accuracy. It also prolongs network lifetime due to its load balancing features. Extensive simulations on both synthetic and real data show that the proposed algorithm significantly outperforms benchmark algorithms.

Conventional power systems have been experiencing transition from centralized supply side management to decentralized supply&demand side management due to power system restructuring and addition of distributed generations (DGs) and Smart Grids (SGs) or Smart Distribution Systems (SDSs) with renewable power sources in the past two decades. Therefore load management under the new operating environment becomes more difficult than that under the conventional environment. This paper presents an Intelligent Metering/Trading/Billing System (ITMBS) and its implementation in Demand Side Load Management (DSLM) of SGs and SDSs. The ITMBS provides real time price information to customers through communication networks. Customers can adjust their demands through setting the operating time of some of the home appliances with energy storage feature such as heaters based on the real time prices to shift their consumptions and save cost. Customers can also participate in Direct Load Control (DLC) program in control center of a micro grid to shift their air condition demands through changing on and off circle based on the real time prices and weather conditions to save energy and to shift system peak load.

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We report an efficient high-peak-power and high-average-power passively Q-switched ytterbium fiber laser with a Cr(4+):YAG crystal as a saturable absorber in an external-resonator configuration. At an incident pump power of 17.5 W, the passively Q-switched fiber laser produces an average power greater than 6.2 W with a pulse repetition rate of 48 kHz. The output pulses noticeably display a mode-locking phenomenon that leads to the maximum peak power to be higher than 20 kW.

The authenticated key exchange (AKE) protocol can ensure secure communication between a client and a server in the electricity transaction of the Energy Internet of things (EIoT). Park proposed a two-factor authentication protocol 2PAKEP, whose computational burden of authentication is evenly shared by both sides. However, the computing capability of the client device is weaker than that of the server. Therefore, based on 2PAKEP, we propose an authentication protocol that transfers computational tasks from the client to the server. The client has fewer computing tasks in this protocol than the server, and the overall latency will be greatly reduced. Furthermore, the security of the proposed protocol is analyzed by using the ROR model and GNY logic. We verify the low-latency advantage of the proposed protocol through various comparative experiments and use it for EIoT electricity transaction systems in a Metaverse scenario.