The Internet of Things (IoT) has been widely adopted for its efficient data processing capabilities, but it also poses significant data security challenges. Attribute-Based Encryption (ABE) has attracted attention for enabling fine-grained, attribute-based access control. However, traditional ABE schemes fall short in revoking decryption privileges and protecting policy privacy, especially in scenarios where users’ private keys are exposed over a long period or sensitive access policies are at risk of leakage. To address these issues, this paper proposes an attribute-based encryption scheme that integrates puncturable encryption with policy hiding. In the proposed scheme, users can independently update their private keys without relying on the key distributor, and can revoke decryption capabilities of old keys by puncturing tags embedded in the ciphertext. This mechanism not only significantly reduces communication overhead between users and IoT devices but also ensures the security of previously generated data even if private keys are compromised. It simplifies key management and provides flexible forward security. Moreover, the scheme achieves full access policy hiding through attribute-mapping obfuscation and leverages the immutability of blockchain to ensure that users can verify the integrity of the policy before decryption, thereby further enhancing data privacy and system trustworthiness. We formally prove the scheme's security under the decisional bilinear Diffie-Hellman assumption. Finally, experimental results demonstrate that our scheme offers advantages in both storage overhead and computational efficiency.

Multiagent Markov decision processes (MMDPs) provide an expressive framework for multiagent planning in stochastic domains. However, exactly solving a large MMDP is often intractable due to the exponential space of joint action. Due to the trade-off nature of trading computation time for solution quality, decentralized subgoal-based tree search (Dec-SGTS) methods have shown great success for MMDPs. Existing Dec-SGTS methods rely on the predefined subgoals and the communication between agents to perform well, which might not hold in real-world MMDP domains where there is no expert knowledge on subgoals and communication resources are limited. In this paper, we relax these assumptions that arrive at an automated and communication-free Dec-SGTS. On the one hand, we first propose an upstream guided subgoal search (UGSS) technique to exploit historical search experience for subgoal discovery. On the other hand, in order to coordinate agents’ behaviors without communication, we further propose an expectation-alignment technique to proactively align agents’ policies with team’s expectations. Finally, we conduct extensive experiments on multirobot box-pushing tasks, and the results show that compared to multiagent planning benchmarks, the proposed communication-free Dec-SGTS method, which does not require any subgoal priors, achieves satisfactory rewards.

Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

Combining simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) is a feasible scheme to enhance the energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy per user. As the proposed EE maximization problem is non-convex and extremely complex, we propose an efficient alternating optimization (AO) algorithm by decoupling the original problem into three subproblems which are tackled iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Numerical results confirm the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.