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With the emergence of the Internet of Things (IoT), a large number of physical objects in daily life have been aggressively connected to the Internet. As the number of objects connected to networks increases, the security systems face a critical challenge due to the global connectivity and accessibility of the IoT. However, it is difficult to adapt traditional security systems to the objects in the IoT, because of their limited computing power and memory size. In light of this, we present a lightweight security system that uses a novel malicious pattern-matching engine. We limit the memory usage of the proposed system in order to make it work on resource-constrained devices. To mitigate performance degradation due to limitations of computation power and memory, we propose two novel techniques, auxiliary shifting and early decision. Through both techniques, we can efficiently reduce the number of matching operations on resource-constrained systems. Experiments and performance analyses show that our proposed system achieves a maximum speedup of 2.14 with an IoT object and provides scalable performance for a large number of patterns.

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In this paper, we investigate parallel implementation techniques for network coding. It is known that network coding is useful for both wired and wireless networks and it also mitigates peer/piece selection problems in P2P file sharing systems. However, due to the decoding complexity of network coding, there have been concerns about adoption of network coding in practical network systems and to improve the decoding performance, the exploitation of parallelism has been proposed previously. In this paper, we argue that naive parallelization strategies of network coding may result in unbalanced workload distribution, and thus, limiting performance improvements. We further argue that a higher performance enhancement can be achieved through balanced partitioning methods in parallelized network coding and propose new parallelization techniques for network coding. Our experiments show that on a quad-core processor system, proposed algorithms exhibit up to 5.69 speedup which is better than the linear speedup with the influence of additional cache. Moreover, on an octal-core system, our algorithms even achieve speedup of 8.46 compared to a sequential network coding and 43.3 percent faster than an existing parallelized technique using 1 Mbytes data with 1;024 Â 1;024 coefficient matrix size.Index Terms-Conversion from sequential to parallel forms, parallel algorithms, concurrent programming, data communications, network communications.

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