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S3 NS2 001 - Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach
In mobile ad hoc networks (MANETs), a primary requirement for the establishment of communication among nodes is that nodes should cooperate with each other. In the presence of malevolent nodes, this requirement may lead to serious security concerns; for instance, such nodes may disrupt the routing process. In this context, preventing or detecting malicious nodes launching grayhole or collaborative blackhole attacks is a challenge. This paper attempts to resolve this issue by designing a dynamic source routing (DSR)-based routing mechanism, which is referred to as the cooperative bait detection scheme (CBDS), that integrates the advantages of both proactive and reactive defense architectures. Our CBDS method implements a reverse tracing technique to help in achieving the stated goal. Simulation results are provided, showing that in the presence of malicious-node attacks, the CBDS outperforms the DSR, 2ACK, and best-effort fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet delivery ratio and routing overhead (chosen as performance metrics).
S3 NS2 002 - Enhanced ANTSEC framework with cluster based cooperative caching in mobile ad hoc networks
In a mobile ad hoc network (MANET), communication between mobile nodes occurs without centralized control. In this environment the mobility of a node is unpredictable; this is considered as a characteristic of wireless networks. Because of faulty or malicious nodes, the network is vulnerable to routing misbehavior. The resource constrained characteristics of MANETs leads to increased query delay at the time of data access. In this paper, AntHocNet+Security (ANTSEC) framework is proposed that includes an enhanced cooperative caching scheme embedded with artificial immune system. This framework improves security by injecting immunity into the data packets, improves the packet delivery ratio and reduces end-to-end delay using cross layer design. The issues of node failure and node malfunction are addressed in the cache management.
S3 NS2 003 - Experimental Assessment of ABNO-Driven Multicast Connectivity in Flexgrid Networks
The increasing demand of internet services is pushing cloud services providers to increase the capacity of their data centers (DC) and create DC federations, where two or more cloud providers interconnect their infrastructures. As a result of the huge capacity required for the inter-DC network, the flexgrid optical technology can be used. In such scenario, applications can run in DCs placed in geographically distant locations, and hence, multicast-based communication services among their components are required. In this paper, we study two different approaches to provide multicast services in multilayer scenarios assuming that the optical network is based on the flexgrid technology: 1) establishing a point-to-multipoint optical connection (light-tree) for each multicast request, and 2) using a multipurpose virtual network topology (VNT) to serve both unicast and multicast connectivity requests. When that VNT is not able to serve an incoming request as a result of lack of capacity, it is reconfigured to add more resources. A control plane architecture based on the applications-based network operations (ABNO) one, currently being standardized by the IETF, is presented; workflows are proposed and PCEP extensions are studied for the considered approaches. The experimental validation is carried-out on a testbed setup connecting Telefonica, CNIT, and UPC premises.
S3 NS2 004 - Game Theoretic Max-logit Learning Approaches for Joint Base Station Selection and Resource Allocation in Heterogeneous Networks
This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibrium point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate those theoretical findings.
S3 NS2 005 - A Scalable, Low-Latency, High-Throughput, Optical Interconnect Architecture Based on Arrayed Waveguide Grating Routers
This paper proposes, simulates, and experimentally demonstrates an optical interconnect architecture for large-scale computing systems. The proposed architecture, Hierarchical Lightwave Optical Interconnect Network (H-LION), leverages wavelength routing in arrayed waveguide grating routers (AWGRs), and computing nodes (or servers) with embedded routers and wavelength-specific optical I/Os. Within the racks and clusters, the interconnect topology is hierarchical all-to-all exploiting passive AWGRs. For the intercluster communication, the proposed architecture exploits a flat and distributed Thin-CLOS topology based on AWGR-based optical switches. H-LION can scale beyond 100 000 nodes while guaranteeing up to 1.83×saving in number of inter-rack cables, and up to 1.5×saving in number of inter-rack switches, when compared with a legacy three-tier Fat Tree network. Network simulation results show a system-wide network throughput reaching as high as 90% of the total possible capacity in case of synthetic traffic with uniform random distribution. Experiments show 97% intra cluster throughput for uniform random traffic, and error-free intercluster communication at 10 Gb/s.
S3 NS2 006 - Analysis of system trustworthiness based on information flow noninterference theory
The trustworthiness analysis and evaluation are the bases of the trust chain transfer. In this paper the formal method of trustworthiness analysis of a system based on the noninterference (NI) theory of the information flow is studied. Firstly, existing methods cannot analyze the impact of the system states on the trustworthiness of software during the process of trust chain transfer. To solve this problem, the impact of the system state on trustworthiness of software is investigated, the run-time mutual interference behavior of software entities is described and an interference model of the access control automaton of a system is established. Secondly, based on the intransitive noninterference (INI) theory, a formal analytic method of trustworthiness for trust chain transfer is proposed, providing a theoretical basis for the analysis of dynamic trustworthiness of software during the trust chain transfer process. Thirdly, a prototype system with dynamic trustworthiness on a platform with dual core architecture is constructed and a verification algorithm of the system trustworthiness is provided. Finally, the monitor hypothesis is extended to the dynamic monitor hypothesis, a theorem of static judgment rule of system trustworthiness is provided, which is useful to prove dynamic trustworthiness of a system at the beginning of system construction. Compared with previous work in this field, this research proposes not only a formal analytic method for the determination of system trustworthiness, but also a modeling method and an analysis algorithm that are feasible for practical implementation.
S3 NS2 007 - Mobile Sink based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks
Energy hole problem is a critical issue for data gathering in Wireless Sensor Networks. Sensors near the static sink act as relays for far sensor and thus will deplete their energy very quickly, resulting energy holes in the sensor field. Exploiting the mobility of a sink has been widely accepted as an efficient way to alleviate this problem. However, determining an optimal moving trajectory for a mobile sink is an NP-hard problem. Thus, this paper proposed a Mobile Sink based adaptive Immune Energy-Efficient clustering Protocol (MSIEEP) to alleviate the energy holes. MSIEEP uses the Adaptive Immune Algorithm (AIA) to guide the mobile sink based on minimizing the total dissipated energy in communication and overhead control packets. Moreover, AIA is used to find the optimum number of Cluster Heads (CHs) to improve the lifetime and stability period of the network. The performance of MSIEEP is compared with the previously published protocols; namely LEACH, LEACHGA, A-LEACH, rendezvous and MIEEPB using Matlab. Simulation results show that MSIEEP is more reliable and energy efficient as compared to other protocols. Furthermore, it improves the lifetime, the stability and the instability periods over the previous protocols, because it always selects CHs from high energy nodes. Moreover, the mobile sink increases the ability of the proposed protocol to deliver packets to the destination.
S3 NS2 008 - Joint Resource Allocation for Throughput Enhancement in Cognitive Radio Femto cell Networks
In cognitive radio femto cell network (CRFN), secondary users (SUs) cooperatively sense a spectrum band to decide the presence of primary network. However, this sensing overhead generally degrades the throughput performance. The prior work, to resolve this problem, proposed algorithms either to decrease the time spent in sensing or to decrease the number of SUs participating in sensing. In this paper, we propose a joint resource allocation (RA) strategy considering the time and energy consumed for spectrum sensing to maximize the throughput while satisfying the target detection performance in CRFN. Furthermore, to reduce the resources used in spectrum sensing additionally, we also adopt the right censored order statistics based cooperative spectrum sensing scheme, which produces the criterion for deciding the set of reporting SUs. By so jointly designing the time and energy for sensing, the proposed joint RA scheme provides the improvement of spectral efficiency. Through simulation results, it is shown that the proposed joint RA scheme exhibits the enhanced performance over the conventional ones in terms of total throughput of secondary networks.
S3 NS2 009 - A survey on the privacy-preserving data aggregation in wireless sensor networks
Wireless sensor networks (WSNs) consist of a great deal of sensor nodes with limited power, computation, storage, sensing and communication capabilities. Data aggregation is a very important technique, which is designed to substantially reduce the communication overhead and energy expenditure of sensor node during the process of data collection in a WSNs. However, privacy-preservation is more challenging especially in data aggregation, where the aggregators need to perform some aggregation operations on sensing data it received. We present a state-of-the art survey of privacy-preserving data aggregation in WSNs. At first, we classify the existing privacy- preserving data aggregation schemes into different categories by the core privacy-preserving techniques used in each scheme. And then compare and contrast different algorithms on the basis of performance measures such as the privacy protection ability, communication consumption, power consumption and data accuracy etc. Furthermore, based on the existing work, we also discuss a number of open issues which may intrigue the interest of researchers for future work.
S3 NS2 0010 - Energy Detection Technique for Adaptive Spectrum Sensing
The increasing scarcity in the available spectrum for wireless communication is one of the current bottlenecks impairing further deployment of services and coverage. The proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. This paper proposes a new strategy to detect adaptively white spaces in the radio spectrum. Such strategy works in Cognitive Radio (CR) networks whose nodes perform spectrum sensing based on energy detection in a cooperative way or not. The main novelty of the proposal is the use of a cost-function that depends upon a single parameter which, by itself, contains the aggregate information about the presence or absence of primary users. The detection of white spaces based on this parameter is able to improve significantly the deflection coefficient associated with the detector, as compared to other state-of-the-art algorithms. In fact, simulation results show that the proposed algorithm outperforms by far other competing algorithms. For example, our proposal can yield a probability of miss-detection 20 times smaller than that of an optimal soft-combiner solution in a cooperative setup with a predefined probability of false alarm of 0.1.