In this paper, we focus on resource allocation schemes for minimizing the energy consumption of subscriber stations (SSs) in uplink flows of the IEEE 802.16 OFDMA systems. The resource allocation schemes assign subcarriers, powers, and data rates to each SS based on the measured signal to noise ratio (SNR) of the uplink channel and predefined modulation and coding scheme as system parameters. Previous research efforts to optimize resource allocation focus on the rate and throughput maximizations, and develop suboptimal heuristic algorithms. However, this paper intends to reduce the energy consumption of SSs by considering the relationship between energy efficiency and resource allocation. In order to clearly formulate the relationship, we use the Multiple Choice Knapsack (MCK) problem, which is proved to be an NP-hard problem. We propose two heuristic schemes to solve the NP-hard problem, which adaptively use the modulation and coding scheme, defined in the IEEE 802.16 OFDMA systems to minimize the required transmission power of each SS. Our simulation results show that the proposed schemes can reduce the energy consumption by up to 53% compared to the channel state information (CSI) scheme, which determines the modulation and coding level only considering the channel state information.

In a large-scale sensor network, replicated hostile nodes may be used for harsh inner attacks. To detect replicas, this paper presents a distributed, deterministic, and efficient approach robust to node compromise attacks without incurring significant resource overheads.

Biologically-inspired approaches are one of the most promising approaches to realize highly-adaptive distributed systems. Biological systems inherently have self-* properties, such as self-stabilization, self-adaptation, self-configuration, self-optimization and self-healing. Thus, the application of biological systems into distributed systems has attracted a lot of attention recently. In this paper, we present one successful result of bio-inspired approach: we propose distributed algorithms for resource replication inspired by the single species population model. Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. In systems using resource replication, generally, a larger number of replicas lead to shorter time to reach a replica of a requested resource but consume more storage of the hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for the resource sharing application. This paper considers the problem for controlling the densities of replicas adaptively in dynamic networks and proposes two bio-inspired distributed algorithms for the problem. In the first algorithm, we try to control the replica density for a single resource. However, in a system where multiple resources coexist, the algorithm needs high network cost and the exact knowledge at each node about all resources in the network. In the second algorithm, the densities of all resources are controlled by the single algorithm without high network cost and the exact knowledge about all resources. This paper shows by simulations that these two algorithms realize self-adaptation of the replica density in dynamic networks.

Optical burst switching (OBS) is a promising approach for the realization of future flexible high-speed optical networks. In particular, a multicast forwarding method for optical bursts is important if an efficient high-speed grid computing network is to be realized. In OBS networks, the number of burst replicas generated at each node is strongly restricted due to optical power impairment of multicast bursts. Moreover, unrestricted replication of multicast bursts at each OBS node may not be advantageous because an increase in the number of multicast bursts within the network causes more frequent deflection forwarding of both multicast and unicast bursts. This paper proposes an efficient hop-by-hop multicast forwarding method for optical bursts, where idle output ports are selected based on scores simply calculated using a routing table that each OBS node holds. This method can mitigate increases in loss rate and transfer delay of multicast bursts, even if the number of burst replicas generated at each OBS node is strongly restricted. Moreover, this method can efficiently mitigate an increase in the number of multicast bursts within the network by avoiding unnecessary replication of multicast bursts at each OBS node. Simulation results show that the proposed method can actually mitigate degradation of the loss rate and transfer delay for multicast bursts under the restricted number of burst replicas at each OBS node. Moreover, when the arrival rate of multicast bursts is large relative to that of unicast bursts, the proposed method is able to improve the loss rates of both multicast and unicast bursts by switching the forwarding method for the multicast bursts to the simple unicast forwarding method without burst replication.

We present a recovery scheme based on Self-protected Spanning Tree (SST), which recovers from failure all by itself. In the recovery scheme, the links are assigned birthdays to denote the order in which they are to be considered for adding to the SST. The recovery mechanism, named Birthday-based Link Replacing Mechanism (BLRM), is able to transform a SST into a new spanning tree by replacing some tree links with some non-tree links of the same birthday, which ensures the network connectivity after any single link or node failure. First, we theoretically prove that the SST-based recovery scheme can be applied to arbitrary two-edge connected or two connected networks. Then, the recovery time of BLRM is analyzed and evaluated using Ethernet, and the simulation results demonstrate the effectiveness of BLRM in achieving fast recovery. Also, we point out that BLRM provides a novel load balancing mechanism by fast changing the topology of the SST.

Many p2p based wide-area storage networks have been proposed to provide scalable storage services by combining the idle resources of many unreliable nodes. These storage networks can also provide highly available and reliable storage services, by replicating each data on several nodes. The popular approach is availability based replication which uses individual node availability. However, some replicas leave within a short time under high churn in p2p networks. This results in heavy and bursty data traffic, and sometimes some data are lost. This paper presents the lifetime-aware replication which uses the lifetime of each node to prevent the bursty failures and the data loss. It keeps a primary replica which has enough time to replace a lost redundancy. It also spreads replicas on the timeline to reduce the overlapped replicas as best as it can. Results from event-driven simulations show that the lifetime-aware replication keeps high data durability with less data traffic.

An important objective of surveillance sensor networks is to effectively monitor the environment, and detect, localize, and classify targets of interest. The optimal sensor placement enables us to minimize manpower and time, to acquire accurate information on target situation and movement, and to rapidly change tactics in the dynamic field. Most of previous researches regarding the sensor deployment have been conducted without considering practical input factors. Thus in this paper, we apply more real-world input factors such as sensor capabilities, terrain features, target identification, and direction of target movements to the sensor placement problem. We propose a novel and efficient hybrid steady-state genetic algorithm giving low computational overhead as well as optimal sensor placement for enhancing surveillance capability to monitor and locate target vehicles. The proposed algorithm introduces new two-dimensional geographic crossover and mutation. By using a new simulator adopting the proposed genetic algorithm developed in this paper, we demonstrate successful applications to the wireless real-world surveillance sensor placement problem giving very high detection and classification rates, 97.5% and 87.4%, respectively.

In a population which consists of a large number of players interacting with each other, the payoff of each player often conflicts with the total payoff of the population which he/she belongs to. In such a situation, a "government" which has the comprehensive perspective is needed to govern the population. Recently, to discuss the population with the government, the authors have proposed replicator dynamics with reallocation of payoffs to analyze an effect of the government. In this model, the government is willing to lead the population to a desirable target state by collecting a part of players' payoffs and reallocating them depending on the target state. The government's action is the rate of collecting payoffs from players and the rate is assumed to be constant and independent of the population state. Thus, in this paper, we suppose that the government change their intervention strategy depending on the current population state. We consider the government as a game player and define the government's payoff as a sum of a benefit and a cost of intervention. We propose a model which describes the evolution of the government's reallocation strategy and investigate stability of its equilibrium points.

This work presents a novel scalable and systolic Montgomery's algorithm in GF(2m). The proposed algorithm is based on the Toeplitz matrix-vector representation, which obtains the scalable and systolic Montgomery multiplier in a flexible manner, and can adapt to the required precision. Analytical results indicate that the proposed multiplier over the generic field of GF(2m) has a latency of d+n(2n+1), where n = m / d , and d denotes the selected digital size. The latency is reduced to d+n(n+1) clock cycles when the field is constructed from generalized equally-spaced polynomials. Since the selected digital size is d ≥5 bits, the proposed architectures have lower time-space complexity than traditional digit-serial multipliers. Moreover, the proposed architectures have regularity, modularity and local interconnect ability, making them very suitable for VLSI implementation.

In this paper, a generalized Montgomery multiplication algorithm in GF(2m) using the Toeplitz matrix-vector representation is presented. The hardware architectures derived from this algorithm provide low-complexity bit-parallel systolic multipliers with trinomials and pentanomials. The results reveal that our proposed multipliers reduce the space complexity of approximately 15% compared with an existing systolic Montgomery multiplier for trinomials. Moreover, the proposed architectures have the features of regularity, modularity, and local interconnection. Accordingly, they are well suited to VLSI implementation.

In a community which consists of a large number of people interacting with each other, social dilemma is an important problem. This problem occurs when the payoff of each person conflicts with the total payoff of the community which he/she belongs to. Evolutionary game theory has been used as a powerful mathematical framework to analyze such a social problem. Recently, the authors have proposed replicator dynamics with reallocation of payoffs. In this model, the government is willing to lead the population to a desirable goal state by using collections and reallocations of payoffs. In this paper, we investigate this model, and show conditions for the goal state to be a locally or a globally asymptotically stable equilibrium point, respectively. We also propose a government's strategy depends on population states which can stabilize the goal state globally.

DHT based p2p systems appear to provide scalable storage services with idle resource from many unreliable clients. If a DHT is used in storage intensive applications where data loss must be minimized, quick replication is especially important to replace lost redundancy on other nodes in reaction to failures. To achieve this easily, a simple replication method directly uses a consistent set, such as a leaf set and a successor list. However, this set is tightly coupled to the current state of nodes and the traffic needed to support this replication can be high and bursty under churn. This paper explores efficient replication methods that only glimpse a consistent set to select a new replica. Replicas are loosely coupled to a consistent set and we can eliminate the compulsory replication under churn. Because of a complication of the new replication methods, the careful data management is needed under churn for the correct and efficient data lookup. Results from a simulation study suggest that our methods can reduce network traffic enormously for high data durability.

Sensor network deployment is very challenging due to the hostile and unpredictable nature of environments. The field coverage of wireless sensor networks (WSNs) can be enhanced and consequently network lifetime can be prolonged by optimizing the sensor deployment with a finite number of mobile sensors. In this paper, we introduce a comprehensive taxonomy for WSN self-deployment in which three sensor relocation algorithms are proposed to match the mobility degree of sensor nodes, particle swarm optimization based algorithm (PSOA), relay shift based algorithm (RSBA) and energy efficient fuzzy optimization algorithm (EFOA). PSOA regards the sensors in the network as a swarm, and reorganizes the sensors by the particle swarm optimization (PSO) algorithm, in the full sensor mobility case. RSBA and EFOA assume relatively limited sensor mobility, i.e., the movement distance is bounded by a threshold, to further reduce energy consumption. In the zero mobility case, static topology control or scheduling schemes can be used such as optimal cluster formation. Simulation results show that our approaches greatly improve the network coverage as well as energy efficiency compared with related works.

Several structured peer-to-peer networks have been created to solve the scalability problem of previous peer-to-peer systems such as Gnutella and Napster. These peer-to-peer networks which support distributed hash table functionality construct a sort of structured overlay network, which can cause a topology mismatch between the overlay and the underlying physical network. To solve this mismatch problem, we propose a topology-aware hierarchical overlay framework for DHTs. The hierarchical approach for the overlay is based on the concept that the underlying global Internet is also a hierarchical architecture, that is, a network of networks. This hierarchical approach for the overlay puts forth two benefits: finding data in a physically near place with a high probability, and smaller lookup time. Our hierarchical overlay framework is different from other hierarchical architecture systems in a sense that it provides a specific self-organizing grouping algorithm. Our additional optimization schemes complete the basic algorithm which constructs a hierarchical structure without any central control.

Accurate extraction of the trap position in the oxide in deep-submicron MOSFET by RTN measurement has been investigated both theoretically and experimentally. The conventional equation based on the ratio of emission time and capture time ignores two effects, that is, the poly gate depletion effect and surface potential variation in strong inversion regime. In this paper, by including both of the two effects, we have derived a new equation which gives us more accurate information of the trap depth from the interface and the trap energy. With experimental result, we compare the trap depth obtained from the new equation and that of the conventional method.

In the age of pervasive computing, ubiquitous collaboration has become an every-day life paradigm. Without an ideal computing infrastructure, issues with ubiquitous collaboration, such as network unreliability, platform heterogeneity, and client's resource constraints, are inevitable. The traditional replication scheme copes with network unreliability by replicating all the objects of a shared application together at once. This is, however, suitable for neither cooperative applications nor mobile computing devices. These problems can be naturally addressed by using a fine-grained replication scheme that enables a portion of the application objects to be replicated. This paper presents an object-oriented middleware that is capable of dynamically and transparently replicating remotely shared Java applications in a partially and on-demand incremental manner. It is also able to maintain various consistency semantics and enables the coexistence of fine-grained replications and conventional remote method invocations. Empirical results indicate several practical benefits of the middleware.

While web proxy caching is a widely deployed technique, the performance of a proxy cache is limited by the local storage. Some studies have addressed this limitation by using the residual resources of clients via a p2p method and have achieved a very high hit rate. However, these approaches treat web objects as homogeneous objects and there is no consideration of various web characteristics. Consequently, the byte hit rate of the system is limited, external bandwidth is wasted, and perceived user latency is increased. The present paper suggests an efficient p2p based web caching technique that manages objects with different policies so as to exploit the characteristics of web objects, such as size and temporal locality. Small objects are stored alone whereas large objects are stored by dividing them into numerous small blocks, which are distributed in clients. On a proxy cache, header blocks of large objects take the place of objects themselves and smaller objects are cached. This technique increases the hit rate. Unlike a web cache, which evicts large objects as soon as possible in the case where clients fulfill the role of backup storage, large objects are given higher priority than small objects in the proposed approach. This maximizes the effect of hits for large objects and thereby increases the byte hit rate. Furthermore, we construct simple latency models for various p2p based web caching systems and analyze the effects of the proposed policies on these systems. We then examine the performances of the efficient policies via a trace driven simulation. The results demonstrate that the proposed techniques effectively enhance web cache performance, including hit rate, byte hit rate, and response time.

We propose a Fault-Tolerant Object Group framework that provides group management and fault-tolerance services for consistency maintenance and state transparency as well. Through a virtual home-network simulation, we validate that the FTOG framework supports both of the reliability and the stability of the distributed home-network systems.

Object caching is a common feature in the scalable distributed object systems. Fine-grained replication optimizes the performance and resource utilization in object caching by enabling a remote object-oriented application to be partially and incrementally on-demand replicated in units of cluster. Despite these benefits, the lack of common and simple implementation framework makes the fine-grained replication scheme not extensively used. This paper proposes the novel frameworks for dynamic, transparent, partial and automatically incremental replication of distributed Java objects based on three techniques that are lazy-object creation, proxy and hook. One framework enables the fine-grained replication of server-side stateful in-memory application, and the other framework enables the fine-grained replication of server-side stateless in-memory application, client-side program, or standalone application. The experimental evaluation demonstrates that the efficiency in terms of response time of both frameworks are relatively practical to the extent of a local method invocation.

In evolutionary game theory, to the best of our knowledge, individuals' perceptions have not been taken into consideration explicitly. When an individual interacts with the other individual under coexistence of heterogeneous sub-populations, the individual may be willing to change his/her strategy depending on the sub-population the other individual belongs to. Moreover, in such a situation, each individual may make an error about the sub-population the other individual belongs to. In this paper, we propose a multi-population model with such erroneous perceptions. We define an evolutionarily stable strategy (ESS) and formulate replicator dynamics in this model, and prove several properties of the proposed model. Moreover, we focus on a two-population chicken game with erroneous perceptions and discuss characteristics of equilibrium points of its replicator dynamics.