6970027
A Dynamic En-Route Scheme for Filtering False Data Injection in Wireless Sensor Networks
In this paper, we propose a dynamic en-route filtering scheme for false data injection attacks in wireless sensor networks. In sensor networks, adversaries can inject false data reports containing bogus sensor readings or nonexistent events from compromised nodes. Such attacks may not only cause false alarms, but also drain out the limited energy of sensor nodes. Several existing schemes for filtering false reports either cannot deal with dynamic topology of sensor networks or have limited filtering capacity. In our scheme, a legitimate report is endorsed by multiple sensing nodes using their own authentication keys generated from one-way hash chains. Cluster head uses Hill Climbing approach to disseminate the authentication keys of sensing nodes to the forwarding nodes along multiple paths toward the base station. Hill Climbing guarantees that the forwarding nodes closer to a cluster hold more authentication keys for the cluster than those nodes farther from it do, hence, the number of keys held by each forwarding node can be balanced. In filtering phase, each forwarding node validates the authenticity of the reports and drops those false ones. Compared to existing schemes, our scheme can better deal with dynamic topology of sensor networks. Analytical and simulation results show that our scheme drops false reports earlier even with a lower memory requirement and tolerates more compromised nodes. Our scheme also outperforms others in term of energy efficiency, especially for large sensor networks.
1959706
A key pre-distribution scheme using deployment knowledge for wireless sensor networks
Sensor networks pose security and privacy challenges. One challenge is how to bootstrap secure communications among nodes. Several key pre-distribution schemes have been proposed, however, they either have a high memory requirement for large networks or need a large transmission range to achieve the desired connectivity. In this paper, we propose a novel key pre-distribution scheme using deployment knowledge. In our scheme, sensor field is divided into multiple hexagonal grids and nodes are divided into groups, each of which is deployed into a grid. By using deployment knowledge, we can drastically reduce the number of potential groups from which neighbors of each node may come. Thus, a pair-wise key can be generated efficiently for each pair of neighbors. Compared to existing schemes, our scheme achieves a high connectivity with a lower memory requirement and a smaller transmission range and outperforms others in resilience against node capture attacks.
32481962
A Hybrid Filter for Program Personalization
1113559
Fuzzy Clustering for TV Program Classification
In order to achieve TV program group recommendation, an approach based on fuzzy clustering is proposed for program classification. We first describe the XML based program description metadata representation, in which both textual and symbolic information is included. Secondly it presents the program feature extraction and presentation method. A program is defined as two vectors, one is based on term statistics implying what the program is about, and the other reflects broadcasting characteristics of the program. Then the classifying approach based on fuzzy clustering is proposed. The approach goes: normalizing original data, building fuzzy similarity matrix, and then clustering. The final fuzzy similarity matrix is constructed by combining two fuzzy similarity matrices calculated from two different aspects.
16105685
A Hybrid Learning Approach for TV Program Personalization
The rapid growth of communication technologies and the invention of set-top-box (STB) and personal digital recorder (PDR) have enabled today’s television to receive and store tremendous programs. The abundance of TV programs precipitates a need for personalization tools to help people obtain programs that they really want to watch. User preference learning plays an important role in TV program personalization. In this paper, we introduce a hybrid user preference learning approach for TV program personalization. The learning architecture is designed to integrate multiple learning sources for preference learning, which are explicit input/modification, user viewing history, and user real-time feedback. Among those, learning from user viewing history and learning from user real-time feedback are described in detail. The experimental results proved that the hybrid learning approach outperforms the learning method merely adopting user real-time feedback.
5533159
Cycles of length 1 modulo 3 in graph
We prove a conjecture of Saito that if a graph G with ≥ 3 has no cycle of length 1 (mod 3), then G has an induced subgraph which is isomorphic to the Petersen graph. The above result strengthened the result by Dean et al. that every 2-connected graph with ≥ 3 has a (1 mod 3)-cycle if G is not isomorphic to the Petersen graph. ? 2001 Elsevier Science B.V. All rights reserved.
17105028
Performance and power analysis of globally asynchronous locally synchronous multiprocessor systems
This paper investigates the performance and power dissipation of globally asynchronous locally synchronous (GALS) multi-processor systems. We show that communication loops are a source of significant throughput degradation in communications links and that there is no degradation whatsoever under certain conditions for one-way links, and that it is possible to design GALS multiprocessors without this performance penalty. Independent clock domains and unbalanced computation in the GALS multiprocessor allow scaling of the clock frequency and supply voltage to achieve high energy efficiency. The synchronization overhead between independent clock domains results in a less than 1% performance reduction compared to a globally synchronous system over a number of DSP and numerical applications. Clock and voltage scaling can achieve an approximately 40% power savings with no reduction of performance. These results compare favorably with the 25% power savings and more than 10% performance reduction reported for GALS uniprocessors