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Abstract. Side-channel attacks (SCAs) exploit weakness in the physical implementation of cryptographic algorithms, and have emerged as a realistic threat to many critical embedded systems. However, no theoretical model for the widely used differential power analysis (DPA) has revealed exactly what the success rate of DPA depends on and how. This paper proposes a statistical model for DPA that takes characteristics of both the physical implementation and cryptographic algorithm into consideration. Our model establishes a quantitative relation between the success rate of DPA and a cryptographic system. The side-channel characteristic of the physical implementation is modeled as the ratio between the difference-of-means power and the standard deviation of power distribution. The side-channel property of the cryptographic algorithm is extracted by a novel algorithmic confusion analysis. Experimental results on DES and AES verify this model and demonstrate the effectiveness of algorithmic confusion analysis. We expect the model to be extendable to other SCAs, and provide valuable guidelines for truly SCA-resilient system design and implementation.

Smart grid is advancing power grids significantly, with higher power generation efficiency, lower energy consumption cost, and better user experience. Microgrid utilizes distributed renewable energy generation to reduce the burden on utility grids. This paper proposes an energy ecosystem; a costeffective smart microgrid based on intelligent hierarchical agents with dynamic demand response (DR) and distributed energy resource (DER) management. With a dynamic update mechanism, DR automatically adapts to users' preference and varying external information. The DER management coordinates operations of micro combined heat and power systems (µCHPs), and vanadium redox battery (VRB) according to DR decisions. A twolevel shared cost-led µCHPs management strategy is proposed to reduce energy consumption cost further. VRB discharging is managed to be environment-adaptive. Simulations and numerical results show the proposed system is very effective in reducing the energy consumption cost while satisfying user's preference. Index Terms-Demand response (DR), distributed energy resources (DER), microgrid, particle swarm optimization (PSO), Q-learning, smart grid. I. INTRODUCTIONW ITH MORE electricity-consuming products coming into daily lives, such as electrical vehicles (EVs) and advanced heating, ventilation, and air conditioning systems, load demand increases dramatically and imposes significant burdens on the existing power grid. Smart grid, integrated with distributed renewable energy generation, advanced metering infrastructure, and information technologies, can cope with the impending global energy crisis and environment deterioration. To achieve high energy efficiency in smart grid, load can be shaved by demand response (DR) and distributed energy resources (DER) have to be well managed. Residential DR can be defined as reactions of users to the time-varying energy price offered by utility companies [1], where schedulable load is shifted to off-peak hours to reduce the energy consumption cost. On the other hand, DERs, including distributed generation (DG) and energy storage system, can be Manuscript

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In the past decade, underwater acoustic sensor networks (UW-ASNs) have been studied broadly in various aquatic applications, enabling humans to observe and explore the vast underwater domain. Although acoustic underwater communications are able to support long-range and low-bandwidth applications, the capabilities of UW-ASN are greatly limited by the long delay and low data rate of acoustic communications. Underwater freespace optical communication is a potential alternative solution. However, it has short transmission ranges and requires dense deployment. In this paper, we propose a novel acoustic-optical hybrid architecture for underwater wireless sensor networks, and a multi-level Q-learning based routing protocol, MURAO, for such networks. The network is physically partitioned into several groups and logically divided into two layers. The upperlayer group leaders supervise the routing in the lower layer, and the lower-layer group members carry out the actual data packet routing. Because upper-layer group leaders have a boarder view of the network and all the groups are able to carry out the learning process concurrently, the performance of routing is greatly improved compared to the flat Q-learning-based routing. The experiment results show that MURAO is more robust to changes of network topology, and achieves much higher delivery rates as well as shorter delays in a dynamic network than the flat Q-learning routing.