The Energy based Ultra-Wideband Multipath Routing（EUMR） algorithm for Ad hoc sensor network is proposed. It utilizes the function of UWB positioning to reduce the network communication delay and route overhead. Furthermore,the algorithm considers energy consumption,the residual energy and node hops of communication paths to make energy consumption more balanced and extend the network lifetime. Then routing which is stable,energy-saving and low-delay is realized. Simulation results show that the algorithm has better performance on saving energy,route overhead,stability and extending network lifetime.

To improve the efficiency of the forensic analysis method on data mining, this paper proposes a new method for the forensic analysis of the behavior profiling on the longest frequent pattern which is constructed by immune clonal algorithm. Taking the behavior data and the candidate pattern of the frequent item sets as the antigen and the antibody respectively, the support of the antigen to the antibody as the function of affinity, the key attribute as the constraint condition, and the minimal support as the screening condition, the behavior profiling on the longest frequent pattern is built with the help of the immune clonal operation to antibody. The abnormal data are detected by the matching method that the audit data pass through the list items of the behavior profiling. The proposed method and the method on Apriori-CGA are applied in the same problem. The comparison results indicate that the setting up time of behavior profiling and the test time of abnormal data are dramaticly reduced. Therefore, the proposed method has a good ability in the efficiency of forensic analysis and electronic crime investigation.

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc.However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes).In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems.First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation.In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given.Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor.Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance.Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

For the intension of buffering resources in network-on-chip(NoC),a buffer allocation algorithm is proposed.Given buffering space budget,our algorithm automatically allocates the resources on each input channel,in different routers across the chip,to match the traffic load,such that the overall performance is maximized.In the algorithm,a novel analytical model for adaptive routing is used to quickly detect potential performance bottlenecks in the system.Simulation results indicate that our algorithm can get lower average packet latency than uniform allocation strategy,and about 33% savings in buffering resources can be achieved.

To make an immune-inspired network intrusion detection system (IDS) effective, this paper proposes a new framework, which includes our avidity-model based clonal selection (AMCS) algorithm as core element. The AMCS algorithm uses an improved representation for antigens (corresponding to network access patterns) and detectors (corresponding to detection rules). In particular, a bio-inspired technique called gene expression programming (GEP) is integrated with artificial immune system (AIS) in detector representation. In addition, inspired by the avidity model of immunology, this paper also defines new avidity/affinity functions (corresponding to the metric for quantify the interactions between detector and antigens) that take the priorities of attribute into account. Accordingly, the proposed algorithm integrates both negative selection and positive selection with a balance factor k to assign appropriate weights to self and non-self avidity. The well known KDD CUP'99 DATA set is used for performance evaluation. The results show that the intrusion detection based on AMCS provides a higher detection rate of DoS attack, a lower false alarm rate, and a lower detectors generation cost. Our results indicate that breaking the bottleneck of immune-inspired network IDS through adjusting basic elements is feasible and effective.

Self-similarity is a ubiquitous phenomena spanning across diverse network environments and has great effect on network performance. The Long-Range Dependence (LRD) structure in self-similar network traffic could be exploited in traffic prediction, which is very useful in resource allocation. But the traffic prediction is very difficulty because of its multi-scale and non-linear feature. Having considered these features of self-similar network traffic, the prediction algorithm with ANN is proposed in this paper. At first, ANN for the multi-scale traffic prediction is constructed. The procession of Input/Output vectors, parameter selection and training scheme are also discussed. Then the artificial traces are generated with Fractional-ARIMA model and are used in the experiments of ANN multi-scale prediction. The result shows that this algorithm can predict the self-similar network traffic at multi-scale. It is very useful to optimize network control scheme and improve network performance.

In order to ensure the reliability of network-on-chip (NoC) under faulty circumstance, a dynamic fault tolerant routing algorithm is proposed. This algorithm can implement detour routing when there are both static and dynamic permanent faults in the network. That means the packet is able to move around the faults to the destination with a non-minimum path. In addition, the multi-level congestion control mechanism gives the algorithm the ability to distribute the load over the whole network and to avoid hotspots around the faults. Simulation results demonstrate the advantage of the proposed routing algorithm in terms of average packet latency and packet loss rate compared with negative-first routing algorithm and DyAD routing algorithm in the presence of permanent faults. For the proposed algorithm, it can get much less average packet latency and lead to less than 20% packet loss rate.

A simple demonstration system for fine tracking is put forward,and the component is presented in detail.The centroid algorithm is the traditional method for beacon,and it has a systematic error and a random one.So it is obligatory to adopt the pixel subtraction.With the PID-like neural network algorithm,the weights of neural network and the parameters could be adjusted to reduce beacon vibration by the function of self-learning and adaptability in real time.The experimental results show the pixel subtraction improves the precision of centroid algorithm,and the PID-like neural network algorithm is robust.The fine tracking system can track 50 Hz beacon vibration with CCD of 400 Hz,and the range of beacon vibration reduces 25.7%,which is beneficial for the system to apply.

The MIMO Free-Space optical communication system with DPIM modulation is presented aiming at atmospheric turbulence and the packet error rate.After established MISO link model,the error probability performance is analyzed with atmospheric turbulent optical channel and APD detector receiver.The optimal soft threshold and packet error rate derive with maximum likelihood detection.The results demonstrate that transmit diversity smooth optical intensity fluctuation,and received diversity increase aperture average to mitigate the effect of its fluctuation,resulting in error-rate-performance improvement.With equal transmit average power and equal background radiation,Opt APD gain is much at one for different MIMO.Compared with PPM(pulse position modulation),DPIM only has a marginally inferior error probability and is superior to OOK(on off key) greatly,but less complicated to implement.

In order to solve the contradiction between the performance and the cost of arbitration scheduling in the multi-port System-on-Chip,a new arbitration strategy expanding the mechanisms of early arbitration and request waiting priority on the fixed priority arbitration algorithm is proposed.In this strategy,the mechanism of early arbitration is used to decide a new bus access request according to the duration of data transfer,and the mechanism of request waiting priority is employed to set the request waiting time for the port that bus access requests turned down by the arbiter.When the waiting time expires,an access priority is provided for the port.Experimental results indicate that,as compared with the arbitration strategy based on request waiting priority,the proposed strategy increases the bus utilization by about 10%;and that it considers both the priority and the fairness of the ports and possesses high system performance with low cost.

The implementation of energy balanced routing is an effective way to prolong the lifetime of wireless sensor networks (WSNs). To balance energy consumption, fuzzy next-hop selecting strategy was designed: nodes of different gradients are fuzzily classified into relevant levels; each level has a chance to provide a node with maximum residual energy as the next-hop. Based on the above strategy and directed diffusion (DD), fuzzy next-hop selection based energy balance (FNSEB) routing protocol was proposed. The simulation results showed that FNSEB utilized the limited energy more thoroughly and rationally, decreased the average energy consumption and prolonged the lifetime of WSNs.

GADD (GA based directed diffusion) protocol was proposed based on the improvement of directed diffusion. In GADD, a detecting packet strategy was designed, by which the initial path series could be built free of blindness at premature stage. To optimize the path series, genetic algorithm was introduced to strengthen its diversity, promote its quality and improve the energy utilization rates of nodes. Simulation results showed that GADD not only prolonged the lifetime of wireless sensor network, decreased the impact of network size on performance, shortened the transmission delay, but also turned out to be of better convergence.

To minimize battery consumption for portable devices, the prescheduling policy of battery-aware scheduling was improved by optimizing slack distribution. A battery-aware compound task scheduling (BACTS) algorithm considering various aspects including task deadline, current and execution time was proposed and evaluated with the previously prevailing earliest deadline first (EDF) algorithm. The results indicate the proposed BACTS algorithm manages to figure out a feasible schedule (if available) in battery-aware task scheduling even for disorganized connected task graphs beyond the solving ability of EDF. Its schedule achieves better performance with lower charge consumption after prescheduling, and also lower or equal optimum charge consumption after voltage scaling.

In order to deal with the limitations during the register transfer level veri. cation, a new functional verification method based on the random testing for the system-level of system-on-chip is proposed. The validity of this method is proven theoretically. Specifically, testcases are generated according to many approaches of randomization. Moreover, the testbench for the system-level veri. cation according to the proposed method is designed by using advanced modeling language. Therefore, under the circumstances that the testbench generates testcases quickly, the hardware/software co-simulation and co-verification can be implemented and the hardware/software partitioning planning can be evaluated easily. The comparison method is put to use in the evaluation approach of the testing validity. The evaluation result indicates that the efficiency of the partition testing is better than that of the random testing only when one or more subdomains are covered over with the area of errors, although the efficiency of the random testing is generally better than that of the partition testing. The experimental result indicates that this method has a good performance in the functional coverage and the cost of testing and can discover the functional errors as soon as possible.

To balance the energy consumption in WSN, residual energy scheming based energy equilibrium routing protocol (RESEE) was proposed. In RESEE, a fuzzy gradient classification based next hop strategy was designed to balance the energy consumption as a whole. Reject and recommend strategy of low residual energy node, and reactivate strategy of high residual energy node were applied to equilibrize the energy consumption locally. Simulation results show that the average dissipated energy decreases by 29% in RESEE compared with directed diffusion (DD), and the lifetime grew by 125% at the network size of 400. Residual energy distribution maps also prove the energy equilibrium characteristic of RESEE.

Dynamic voltage scaling (DVS) is an efficient approach to maximize the battery life of portable devices. A novel overall planning strategy (OPS II) balancing slack supply and demand for DVS is proposed. An OPS II-based slack-nibbling overall planning strategy (SNOPS) algorithm is also proposed, which iteratively nibbles slacks for appropriate tasks selected by an overall planning dynamic priority function to perform DVS until the slack is exhausted and an optimum voltage setting is obtained. For a high-load task set, SNOPS manages to recover battery overload while maintaining schedulability. For random variable-load task sets, SNOPS achieves a saving of 29.51% battery capacity on average, the suboptimal gap is 27.84% narrower than that of our previously proposed OPS-based algorithm, and 92.10% narrower than that of the algorithm proposed by Chowdhury et al. Results indicate that OPS II manages to save battery to various extents while maintaining schedulability, and demonstrates good load compatibility and close-to-optimal performance on average.

Purpose - The purpose of this paper is to search an energy balance routing in the wireless sensor networks (WSN) and lengthen the life of the networks.Design/methodology/approach - To save energy in the WSN, some routing protocols search routing with the minimum total energy consumption of the network, and others reduce data redundancy by data aggregation. But if the distribution of energy consumption was not even, the energy of some nodes would be exhausted rapidly and thus the whole network would break down. Thus, an energy balance routing notion, including communication energy cost of the routing, remaining energy of communication sensors and sensor load have been involved. Then a new algorithm, mouse colony optimization and simulated annealing (SA), is advanced to solve the problem of energy balance routing in the network.Findings - The energy balance routing, based on mouse colony optimization and SA, performs well and yields better performance than other congener algorithms.Research limitations/implications - The appointed times of the algorithm is the main limitation which increase the complexity of the algorithm.Practical implications - A very useful routing in wireless sensor networks.Originality/value - The new approach of energy balance routing notion, including communication energy cost of the routing, remaining. energy of communication sensors and sensor load. The new algorithm, mouse colony optimization algorithm, simulated mice action, was proposed to solve the energy balance routing of the network.

A virtual channel allocation algorithm for wormhole routing networks-on-chip is proposed.Traditionally,the virtual channels are allocated uniformly,which results in a waste of area and power.To remedy this situation,based on the queuing theory,we propose a router analytical model.Using this model,the proposed algorithm calculates the bandwidth usage at each router in the network according to the traffic characteristics of the target application,and adds virtual channels(VCs) only to the channels with the highest bandwidth usage.The simulation results show that the virtual channel(VC) allocation result is more reasonable and higher total transmission rate can be achieved compared to the uniform VC allocation.For hotspot traffic,about 33.3 % savings in buffering resources can be achieved using our algorithm,in the case of achieving similar performance levels.

Clustering process optimized by particle swarm optimizer makes for topology control and energy conservation in wireless sensor networks. The particle fitness is defined as its neighborpsilas information including location and residual energy, a novel distributed clustering approach based on discrete particle swarm optimization (DPSO) for single-hop routing protocol is developed, named as DPSOCA. The effects of inertia weight on network lifetime and energy efficiency are studied. A set of optimal cluster heads are dynamically created using elected method that DPSOCA has minimum energy dissipation, decreases the randomicity of node being cluster head, and outperforms LEACH with significantly prolonging the networks lifetime and efficiently balancing the networks energy-dissipation. The results show that a greater randomicity of inertia weight has a higher cost of performance of network lifetime to convergence iterations.