The proliferation of decentralized applications across different autonomous blockchains raises the need to enable cross-chain data interoperability (CCDI). However, prior approaches for supporting CCDI often hit scalability bottlenecks regarding critical metrics, e.g., memory, or remain prone to withholding and censorship attacks. This paper proposes two protocols to implement secure and efficient CCDI under adversarial conditions. The cross-chain token exchange (CCTE) protocol for atomic swaps is proposed. It adopts a deposit mechanism, a blockchain-of-blockchains (BoB), and Merkle proofs to ensure the completion of token exchanges even under withholding attacks. It utilizes a parallelized design to support concurrent token exchanges, thereby improving its efficiency and avoiding censorship attacks that target sequential token exchanges. The CCDI protocol is proposed to support any CCDI application. It authorizes a unique BoB to execute arbitrary CCDI application logic. It integrates a “transfer and in place data update” mechanism to improve its efficiency, and this mechanism enables a blockchain update its state data items using a single transaction, without requiring any information from other blockchains. Moreover, the CCDI protocol integrates a state data migration scheme, which supports a user to migrate its state data item to censorship-resilient blockchains, and incorporates a malicious user nodes elimination scheme, which enables the updates of state data items in a CCDI process even under withholding attacks. Systematic performance evaluations are conducted to compare the two protocols with existing ones. The CCTE protocol reduces latency by at least 52% compared to existing protocols under probabilistic consensus setting. The CCDI protocol outperforms prior protocols, lowering communication cost by 59%, computation overhead by 41%, memory burden by 12%, and latency cost by 33%.

Ransomware attacks have become a critical threat in human society. This article proposes a multistage game for the ransomware attacker and the target. The game consists of four subgames, which comprehensively model the decision-making of the attacker and the target in different stages of a ransomware attack event (i.e., the data backup stage, ransomware development stage, compromise stage, and data release stage). A backward induction-based analysis framework is developed to obtain the equilibrium of each subgame. Extensive experiments are conducted to investigate the optimal decisions of the attacker and the target under different game conditions. From the theoretical analysis and experiments, practical strategies are summarized for relevant parties in a ransomware attack event. For example: performing ex-ante data backup, promptly calling the executive branch, and actively negotiating with the attacker are the most effective ways for reducing the loss of the target, while carefully choosing the target for launching the attack and proposing a reasonable ransom amount can effectively increase the attacker's benefits. These analysis and experiment results can provide a useful reference to understand ransomware attacks and take measures to reduce or mitigate their negative impact on human society.

Residential demand response (DR) is recognized as a promising approach to improve grid energy efficiency and relieve the network stress. Many studies have been conducted to design home energy management systems that directly schedule and control the household appliances. Distinguished from existing works, this article proposes a personalized recommendation system (PRS) to learn energy-efficient household appliance usage experiences from a large scale of residential users, and recommends suitable appliance usage plans to users while taking their lifestyles into account. The proposed system is based on a collaborative filtering recommendation technique. The PRS first classifies a collection of users as “highly responsive users” and “less responsive users” based on their DR degree analysis. Then, for each less responsive user, the PRS infers the user's lifestyle from usage profiles of nonshiftable appliances and finds out users who have similar habits with the target user from the set of highly responsive users. Based on this, the PRS evaluates the lifestyle similarity between the target user and each smart user, aggregates the appliance usage experiences of highly responsive users, and makes appliance-use recommendations to the target user. Experiments based on a residential data simulator “SimHouse” are designed to validate the proposed system.

Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting, day-ahead scheduling, and actual operation. In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets.

Driven by the energy crisis and global warming problem, smart grid was proposed in the early 21th century as a solution for the sustainable development of human society. With the two-way communication infrastructure available in smart grids, a current challenge is to interpret and gain knowledge from the collected grid big data to optimize grid operations. Service recommendation techniques provide promising tools to discover knowledge from the grid data, and recommend energy-aware products/services/suggestions to the smart grid participators. This paper is among the first to investigate the prospective of introducing service recommendation techniques into the smart grid demand side management (DSM). In the first part of the paper, the backgrounds of smart grid DSM and service recommendation techniques are reviewed, followed by the presentation and discussion of key technologies that can facilitate the development of smart grid recommender systems. An outline on potential application scenarios of smart grid recommender systems as well as future challenges are also provided.