A novel combinatorial optimization algorithm called "Gradual neural network (GNN)" is presented for NP-complete broadcast scheduling problems in packet radio (PR) networks. A PR network provides data communications services to a set of geographically distributed nodes through a common radio channel. A time division multiple access (TDMA) protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots called a TDMA cycle. Given a PR network, the goal of GNN is to find a TDMA cycle with the minimum delay time for each node to broadcast packets. GNN for the N-node-M-slot TDMA cycle problem consists of a neural network with N M binary neurons and a gradual expansion scheme. The neural network not only satisfies the constraints but also maximizes transmissions by two energy functions, whereas the gradual expansion scheme minimizes the cycle length by gradually expanding the size of the neural network. The performance is evaluated through extensive simulations in benchmark instances and in geometric graph instances with up to 1000 vertices, where GNN always finds better TDMA cycles than existing algorithms. The result in this paper supports the credibility of our GNN algorithm for a class of combinatorial optimization problems.

A variety of real-time multicast applications such as video conferences, remote lectures, and video-on-demand have become in commonplace with the expansion of broadband Internet services. Due to nontrivial problems in the IP multicast technology, the peer-to-peer multicast technology (P2P-multicast) has emerged as a practical implementation, although its network resource utilization is less efficient. A multihome network has the potential of alleviating this inefficiency by providing flexibility in communication path selections for each host with multiple gateways to the Internet. This paper has first formulated the P2P-multicast routing problem in the multihome network, and has proved the NP-completeness of its decision problem. Then, a two-stage heuristic algorithm called P2PMM_router has been presented for this P2P Multicast Multihome-network routing problem. The first stage constructs an initial multicast routing tree from an optimum spanning tree by Prim algorithm, through satisfying the constraints. The second stage improves the tree by repeating partial modifications and constraint satisfactions. The extensive simulation results using random network instances support the effectiveness of our P2PMM_router.

To reduce the damage of key exposures, forward-secure group signature schemes have been first proposed by Song. In the forward-secure schemes, a secret key of a group member is updated by a one-way function every interval and the previous secret key is erased. Thus, even if a secret key is exposed, the signatures produced by the secret keys of previous intervals remain secure. Since the previous forward-secure group signature schemes are based on the strong RSA assumption, the signatures are longer than pairing-based group signatures. In addition, the complexity of the key update or signing/verification is O(T), where T is the total number of intervals. In this paper, a forward-secure group signature scheme from pairings is proposed. The complexity of our key update and signing/verification is O(log T).

For efficient use of limited electromagnetic wave resource, the assignment of communication channels to call requests is very important in a cellular network. This task has been formulated as an NP-hard combinatorial optimization problem named the channel assignment problem (CAP). Given a cellular network and a set of call requests, CAP requires to find a channel assignment to the call requests such that three types of interference constraints between channels are not only satisfied, but also the number of channels (channel span) is minimized. This paper presents an iterative search approximation algorithm for CAP, called the Quasi-solution state evolution algorithm for CAP (QCAP). To solve hard CAP instances in reasonable time, QCAP evolutes quasi-solution states where a subset of call requests are assigned channels and no more request can be satisfied without violating the constraint. QCAP is composed of three stages. The first stage computes the lower bound on the channel span for a given instance. After the second stage greedily generates an initial quasi-solution state, the third stage evolutes them for a feasible channel assignment by iteratively generating best neighborhoods, with help of the dynamic state jump and the gradual span expansion for global convergence. The performance of QCAP is evaluated through solving benchmark instances in literature, where QCAP always finds the optimum or near-optimum solution in very short time. Our simulation results confirm the extensive search capability and the efficiency of QCAP.

Previously, we have proposed an access point (AP) allocation algorithm in indoor environments for the Wireless Internet-access Mesh NETwork (WIMNET) using one gateway (GW) to the Internet. WIMNET consists of multiple APs that are connected wirelessly mainly by the Wireless Distribution System (WDS), to expand the coverage area inexpensively and flexibly. In this paper, we present two extensions of this algorithm to enhance the applicability to the large-scale WIMNET. One is the multiple GW extension of the algorithm to increase the communication bandwidth with multiple GWs, where all the rooms in the network field are first partitioned into a set of disjoint GW clusters and then, our previous allocation algorithm is applied to each GW cluster sequentially. The APs in a GW cluster share the same GW. The other is the dependability extension to assure the network function by maintaining the connectivity and the host coverage, even if one link/AP fault occurs, where redundant APs are added to the AP allocation by our previous algorithm. The effectiveness of our proposal in terms of the number of APs and the throughput is verified through simulations using the WIMNET simulator.

An approach of membership revocation in group signatures is verifier-local revocation (VLR for short). In this approach, only verifiers are involved in the revocation mechanism, while signers have no involvement. Thus, since signers have no load, this approach is suitable for mobile environments. Although Boneh and Shacham recently proposed a VLR group signature scheme from bilinear maps, this scheme does not satisfy the backward unlikability. The backward unlinkability means that even after a member is revoked, signatures produced by the member before the revocation remain anonymous. In this paper, we propose VLR group signature schemes with the backward unlinkability from bilinear maps.

This paper presents a heuristic graph coloring algorithm named MIPS_CLR, a MInimal-state Processing Search algorithm for the graph CoLoRing problem. Given a graph G(V, E), the goal of this NP-complete problem is to find a color assignment to every vertex in V such that any pair of adjacent vertices must not receive the same color but also the total number of colors should be minimized. The graph coloring problem has been widely studied due to its large number of practical applications in various fields. In MIPS_CLR, a construction stage first generates an initial minimal state composed of as many as colored vertices by a simple greedy algorithm, after a maximal clique of G is found by a maximum clique algorithm. Then, a refinement stage iteratively seeks a solution state while keeping minimality in terms of a cost function by a minimal-state transition method. In this method, the schemes of a best color selection, a random color selection, a color assignment shuffle, and a gradual color expansion are used together to realize the discrete descent search with hill-climbing capabilities. The performance of MIPS_CLR is evaluated through solving DIMACS benchmark graph instances, where the solution quality is generally better than existing algorithms while the computation time is comparable with the best existing one. In particular, MIPS_CLR provides new lower bound solutions for several instances. The simulation results confirm the extensive search capability of our MIPS_CLR approach for the graph coloring problem.

A clique of a graph G(V,E) is a subset of V such that every pair of vertices is connected by an edge in E. Finding a maximum clique of an arbitrary graph is a well-known NP-complete problem. Recently, several polynomial time energy-descent optimization algorithms have been proposed for approximating the maximum clique problem, where they seek a solution by minimizing the energy function representing the constraints and the goal function. In this paper, we propose the binary neural network as an efficient synchronous energy-descent optimization algorithm. Through two types of random graphs, we compare the performance of four promising energy-descent optimization algorithms. The simulation results show that RaCLIQUE, the modified Boltzmann machine algorithm, is the best asynchronous algorithm for random graphs, while the binary neural network is the best one for k random cliques graphs.

The wavelength-division multiplexing (WDM) technology has been popular in communication societies for providing very large communication bands by multiple lightpaths with different wavelengths on a single optical fiber. Particularly, a double-ring optical network architecture based on the packet-over-WDM technology such as the HORNET architecture, has been extensively studied as a next generation platform for metropolitan area networks (MANs). Each node in this architecture is equipped with a wavelength-fixed optical-drop and a fast tunable transmitter so that a lightpath can be established between any pair of nodes without wavelength conversions. In this paper, we formulate the optical-drop wavelength assignment problem (ODWAP) for efficient wavelength reuse under heterogeneous traffic in this network, and prove the NP-completeness of its decision problem. Then, we propose a simple heuristic algorithm for the basic case of ODWAP. Through extensive simulations, we demonstrate the effectiveness of our approach in reducing waiting times for packet transmissions when a small number of wavelengths are available to retain the network cost for MANs.

In anonymous reputation systems, where after an interaction between anonymous users, one of the users evaluates the peer by giving a rating. Ratings for a user are accumulated, which becomes the reputation of the user. By using the reputation, we can know the reliability of an anonymous user. Previously, anonymous reputation systems have been proposed, using an anonymous e-cash scheme. However, in the e-cash-based systems, the bank grasps the accumulated reputations for all users, and the fluctuation of reputations. These are private information for users. Furthermore, the timing attack using the deposit times is possible, which makes the anonymity weak. In this paper, we propose an anonymous reputation system, where the reputations of users are secret for even the reputation manager such as the bank. Our approach is to adopt an anonymous credential certifying the accumulated reputation of a user. Initially a user registers with the reputation manager, and is issued an initial certificate. After each interaction with a rater, the user as the ratee obtains an updated certificate certifying the previous reputation summed up by the current rating. The update protocol is based on the zero-knowledge proofs, and thus the reputations are secret for the reputation manager. On the other hand, due to the certificate, the user cannot maliciously alter his reputation.

In this paper we show that the neuron filter is effective for relaxing the coefficient sensitiveness of the Hopfield neural network for combinatorial optimization problems. Since the parameters in motion equation have a significant influence on the performance of the neural network, many studies have been carried out to support determining the value of the parameters. However, not a few researchers have determined the value of the parameters experimentally yet. We show that the use of the neuron filter is effective for the parameter tuning, particularly for determining their values experimentally through simulations.

With the explosive growth of the Internet system, demands for broadband communication networks have rapidly increased to provide high quality network services. For this purpose, the IEEE 802.6 MAC standard protocol defines the distributed-queue dual bus (DQDB) for metropolitan area networks (MANs). The isochronous channel reuse problem (ICRP) has been studied for efficient use of DQDB by finding proper channel assignments to incoming connection requests. In this paper, we first define the generalized isochronous channel reuse problem (GICRP) as a generalization of ICRP, to afford demands of simultaneously satisfying plural connection requests such as for multicast applications, where certain sets of connection requests must be assigned channels simultaneously. We prove the NP-completeness of its decision problem. Then, we propose a minimum dead space (MDS) algorithm as a heuristic approach to GICRP. The extensive simulation results show that with shorter computation time, our MDS algorithm can always find better channel assignments reducing the waiting time for packet transmissions than the best existing algorithm for conventional ICRP.

Nowadays, with spreads of inexpensive small communication devices, a number of wireless local area networks (WLANs) have been deployed even in the same building for the Internet access services. Their wireless access-points (APs) are often independently installed and managed by different groups such as departments or laboratories in a university or a company. Then, a user host can access to multiple WLANs by detecting signals from their APs, which increases the energy consumption and the operational cost. It may also degrade the communication performance by increasing interferences. In this paper, we present an AP aggregation approach to solve these problems in multiple WLAN environments by aggregating deployed APs of different groups into limited ones using virtual APs. First, we formulate the AP aggregation problem as a combinatorial optimization problem and prove the NP-completeness of its decision problem. Then, we propose its heuristic algorithm composed of five phases. We verify the effectiveness through extensive simulations using the WIMNET simulator.

An approximation algorithm composed of a digital neural network (DNN) and a modified greedy algorithm (MGA) is presented for the board-level routing problem (BLRP) in a logic emulation system based on field-programmable gate arrays (FPGA's) in this paper. For a rapid prototyping of large scale digital systems, multiple FPGA's provide an efficient logic emulation system, where signals or nets between design partitions embedded on different FPGA's are connected through crossbars. The goal of BLRP, known to be NP-complete in general, is to find a net assignment to crossbars subject to the constraint that all the terminals of any net must be connected through a single crossbar while the number of I/O pins designated for each crossbar m is limited in an FPGA. In the proposed combination algorithm, DNN is applied for m = 1 and MGA is for m 2 in order to achieve the high solution quality. The DNN for the N-net-M-crossbar BLRP consists of N M digital neurons of binary outputs and range-limited non-negative integer inputs with integer parameters. The MGA is modified from the algorithm by Lin et al. The performance is verified through massive simulations, where our algorithm drastically improves the routing capability over the latest greedy algorithms.

Group signature schemes with membership revocation have been intensively researched. However, signing and/or verification of some existing schemes have computational costs of O(R), where R is the number of revoked members. Existing schemes using a dynamic accumulator or a similar technique have efficient signing and verifications with O(1) complexity. However, before signing, the signer has to modify his secret key with O(N) or O(R) complexity, where N is the group size. Therefore, for larger groups, signers suffer from enormous costs. On the other hand, an efficient scheme for middle-scale groups with about 1,000 members is previously proposed, where the signer need not modify his secret key. However this scheme also suffers from heavy signing/verification costs for larger groups with more than 10,000 members. In this paper, we adapt the middle-scale scheme to larger groups ranging from 1,000 to 1,000,000 members. At the sacrifice of the group manager's slight cost, our signing/verification is sufficiently efficient.

Previously Verifier-Local Revocation (VLR) group signature schemes from bilinear maps were proposed. In VLR schemes, only verifiers are involved in the revocation of a member, while signers are not. Thus, the VLR schemes are suitable for mobile environments. Furthermore, the previously proposed schemes satisfy the important backward unlinkability. This means that even after a member is revoked, signatures produced by the member before the revocation remain anonymous. This property is needed in case of a voluntary leave of a member or in case of a key loss. However, in the previous schemes, signatures become long, due to the adopted assumption, which should be improved in order to apply the schemes to the mobile environments. In this paper an improved VLR scheme is proposed with the shorter group signatures. This is achieved by using a different assumption, DLDH assumption, and improving zero-knowledge proofs in the group signatures. The length of the proposed group signatures is reduced to about 53% of that of the previous ones.

This paper presents an improved neural network for channel assignment problems in cellular mobile communication systems in the new co-channel interference model. Sengoku et al. first proposed the neural network for the same problem, which can find solutions only in small size cellular systems with up to 40 cells in our simulations. For the practical use in the next generation's cellular systems, the performance of our improved neural network is verified by large size cellular systems with up to 500 cells. The newly defined energy function and the motion equation with two heuristics in our neural network achieve the goal of finding optimum or near-optimum solutions in a nearly constant time.

A digital neural network approach is presented for the multilayer channel routing problem with the objective of crosstalk minimization in this paper. As VLSI fabrication technology advances, the reduction of crosstalk between interconnection wires on a chip has gained important consideration in VLSI design, because of the closer interwire spacing and the circuit operation at higher frequencies. Our neural network is composed of N M L digital neurons with one-bit output and seven-bit input for the N-net-M-track-2L-layer problem using a set of integer parameters, which is greatly suitable for the implementaion on digital technology. The digital neural network directly seeks a routing solution of satisfying the routing constraint and the crosstalk constraint simultaneously. The heuristic methods are effectively introduced to improve the convergence property. The performance is evaluated through solving 10 benchmark problems including Deutsch difficult example in 2-10 layers. Among the existing neural networks, the digital neural network first achieves the lower bound solution in terms of the number of tracks in any instance. Through extensive simulation runs, it provides the best maximum crosstalks of nets for valid routing solutions of the benchmark problems in multilayer channels.

A neural network approach called the "Gradual Neural Network (GNN)" for the time slot assignment problem in the TDM multicast switching system is presented in this paper. The goal of this NP-complete problem is to find an assignment of packet transmission requests into a minimum number of time slots. A packet can be transmitted from one source to several destinations simultaneously by its replication. A time slot represents a switching configuration of the system with unit time for each packet transmission through an I/O line. The GNN consists of the binary neural network and the gradual expansion scheme. The binary neural network satisfies the constraints imposed on the system by solving the motion equation, whereas the gradual expansion scheme minimizes the number of required time slots by gradually expanding activated neurons. The performance is evaluated through simulations in practical size systems, where the GNN finds far better solutions than the best existing altorithm.

A constraint resolution scheme in the Hopfield-type neural network named "Neuron Filter" is presented for efficiently solving combinatorial optimization problems. The neuron filter produces an output that satisfies the constraints of the problem as best as possible according to both neuron inputs and outputs. This paper defines the neuron filter and shows its introduction into existing neural networks for N-queens problems and FPGA board-level routing problems. The performance is evaluated through simulations where the results show that our neuron filter improves the searching capability of the neural network with the shorter computation time.