In this paper, we aim to minimize in-body sensor node energy consumption and to prolong the network lifetime by utilizing a relay strategy based on our proposed flexible quality of service (QoS) radio frequency communication module and advanced In-to-out body path loss (PL) model. A selection algorithm is derived and investigated in which the relay nodes with lower energy consumption and minimum distance to the coordinator will be chosen in each round. In this way, time division multiple access (TDMA) can be investigated to schedule data transmission from implants to the corresponding relays, and thus minimize the overall length of communication links. Moreover, a linear programming network lifetime model is proposed along with various subjective functions. Followed by constraints, simulations are conducted on a proposed topology network assuming two commercial transceivers. Results show that the existing two-relay based protocol achieves higher transmission data rates due to the availability of redundant communication pathways. However, our proposed incremental relay-based cooperative routing protocol outperforms the existing two-relay based scheme regarding total network lifetime, overall throughput, average power consumption and propagation delay.

Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra's algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.

A stochastic resonator has been considered as an alternative signal processing tool because of its noise‐induced performance enhancement ability. Here, the resonator parameters, steady states and transition time of the system are redefined for bipolar binary pulse amplitude modulated (BPAM) signals such that the region in which the resonator benefits from noise can be identified. Simple parameter‐induced SR (PSR) designs are then built, based on this analysis in order to configure the resonator in the optimum region. Furthermore, sine‐induced SR based on using a periodic signal instead of noise is introduced to enhance the system performance and compared with noise‐enhanced SR (NSR). It is shown that sine‐induced SR provides a performance enhancement as it needs less power and does not require an adjustment relevant to the background noise. The results indicate that a resonator improves the receiver performance by eliminating noise if its parameters and BPAM characteristics are set accurately as given in the PSR designs, otherwise the resonator can benefit from either a noise as in NSR, or a sine wave as proposed.

Enhancing the communications performance of diffusion based molecular communications system whilst being considerate about the energy requirements is an ever present problem for researchers. Here it is proposed for the first time, that self-orthogonal convolutional codes (SOCCs) might be a candidate solution moving forward. Analysis is conducted that shows the performance comparisons between both un-coded systems and those which employ Hamming codes. The specific metrics used herein are with respect to coding gain and critical distance. The results show that a (3,2,13) SOCC provides the largest coding gain compared with all Hamming codes over a BER range of 10-5to 10-9and furthermore, also has the shortest critical distance.

Molecules are often used as the information carrier to accomplish communications among nano-machines. To achieve a fast and reliable transmission, knowing the distance between the transmitter (TX) and the receiver (RX) is of great importance. In this paper, an algorithmic distance estimation scheme is proposed whereby, the RX estimates the distance by way of counting the number of molecules within a fixed period of time. Two optimisation methods have been also proposed and explained. Simulations are carried out to show the accuracy and to discover how the diffusion channel will influence the performance. Results illustrate that this estimation scheme and proposed optimisation methods will bring an enhancement to the area of molecular communications.

Future applications for nano-machines, such as drug-delivery and health monitoring, will require robust communications and nanonetworking capabilities. This is likely to be enabled via the use of molecules, as opposed to electromagnetic waves, acting as the information carrier. To enhance the reliability of the transmitted data, Euclidean geometry low density parity check (EG-LDPC) and cyclic Reed-Muller (C-RM) codes are considered for use within a molecular communication system for the first time. These codes are compared against the Hamming code to show that an s = 4 LDPC (integer s ≥ 2) has a superior coding gain of 7.26 dBs. Furthermore, the critical distance and energy cost for a coded system are also taken into account as two other performance metrics. It is shown that when considering the case of nano-to nano-machines communication, a Hamming code with m = 4, (integer m ≥ 2) is better for a system operating between 10-6and 10-3bit error rate (BER) levels. Below these BERs,s = 2 LDPC codes are superior, exhibiting the lowest energy cost. For communication between nano-to macro-machines, and macro-to nano-machines, s = 3 LDPC and s = 2 LDPC are the best options respectively.

Inspired by the temporal receding horizon control in control engineering, this paper reports a novel spatial receding horizon control (SRHC) strategy to partition the facility location optimization problem (FLOP), in order to reduce the complexity caused by the problem scale. Traditional problem partitioning methods can be viewed as a special case of the proposed SRHC, i.e., one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of the route network where a given number of facilities need to be set up. With SRHC to partition the FLOP, genetic algorithm (GA) is integrated as optimizer to resolve the partitioned problem within each spatial receding horizon. On one hand, SRHC helps to improve the scalability of GA. On the other, the population feature of GA helps to reduce the shortsighted performance of SRHC. The effectiveness and efficiency of the reported SRHC and GA for the FLOP are demonstrated by comparative simulation results.

Existing methods for multi-objective optimization usually provide only an approximation of a Pareto front, and there is little theoretical guarantee of finding the real Pareto front. This paper is concerned with the possibility of fully determining the true Pareto front for those continuous multi-objective optimization problems for which there are a finite number of local optima in terms of each single objective function and there is an effective method to find all such local optima. To this end, some generalized theoretical conditions are firstly given to guarantee a complete cover of the actual Pareto front for both discrete and continuous problems. Then based on such conditions, an effective search procedure inspired by the rising sea level phenomenon is proposed particularly for continuous problems of the concerned class. Even for general continuous problems to which not all local optima are available, the new method may still work well to approximate the true Pareto front. The good practicability of the proposed method is especially underpinned by multi-optima evolutionary algorithms. The advantages of the proposed method in terms of both solution quality and computational efficiency are illustrated by the simulation results.

ABSTRACT The channel capacity and the bit error rate of ultra‐wide bandwidth transmitted‐reference systems are optimized with respect to time delay between the reference and the adjacent data pulses. Approximate and theoretical expressions for the signal‐to‐interference‐plus‐noise ratio are derived. Numerical results show that optimizing the receiver can provide a significant capacity improvement of up to 2.2 bits/s/GHz and a bit error rate performance gain of up to 2 dB in effective signal‐to‐noise ratio. Copyright © 2011 John Wiley & Sons, Ltd.

The emerging field of communications between nanoscale devices is one of considerable importance since it is essential that nanonetworks are formed to realize the potential of such devices. Molecular communication is a method based on diffusion, inspired by biological systems and useful over distances in the nm to μm range. Messenger molecules propagate via diffusion and there is thus a probability that they do not arrive at the receiver or are delayed so as to be delivered in the wrong communication time slot. In this paper, the use of error correction codes is considered to improve the transmission performance of molecular communications. Using a simple block code, it is possible to deliver a coding gain of ~1.6 dB. Nevertheless, energy is required for the coding and decoding when employing the code and this paper also considers this for the first time. It is shown that simple error correction delivers a benefit in terms of energy consumption for distances upwards of approximately 10 μm to 20 μm.

OBJECTIVE: An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in BCI research and have numerous successful applications. However, one of the drawbacks of conventional ANNs is the lack of an explicit input optimization mechanism. In addition, results of ANN learning are usually not easily interpretable. In this paper, we have applied an ANN-based method, the genetic neural mathematic method (GNMM), to two EEG channel selection and classification problems, aiming to address the issues above. METHODS AND MATERIALS: Pre-processing steps include: least-square (LS) approximation to determine the overall signal increase/decrease rate; locally weighted polynomial regression (Loess) and fast Fourier transform (FFT) to smooth the signals to determine the signal strength and variations. The GNMM method consists of three successive steps: (1) a genetic algorithm-based (GA-based) input selection process; (2) multi-layer perceptron-based (MLP-based) modelling; and (3) rule extraction based upon successful training. The fitness function used in the GA is the training error when an MLP is trained for a limited number of epochs. By averaging the appearance of a particular channel in the winning chromosome over several runs, we were able to minimize the error due to randomness and to obtain an energy distribution around the scalp. In the second step, a threshold was used to select a subset of channels to be fed into an MLP, which performed modelling with a large number of iterations, thus fine-tuning the input/output relationship. Upon successful training, neurons in the input layer are divided into four sub-spaces to produce if-then rules (step 3). Two datasets were used as case studies to perform three classifications. The first data were electrocorticography (ECoG) recordings that have been used in the BCI competition III. The data belonged to two categories, imagined movements of either a finger or the tongue. The data were recorded using an 8 × 8 ECoG platinum electrode grid at a sampling rate of 1000 Hz for a total of 378 trials. The second dataset consisted of a 32-channel, 256 Hz EEG recording of 960 trials where participants had to execute a left- or right-hand button-press in response to left- or right-pointing arrow stimuli. The data were used to classify correct/incorrect responses and left/right hand movements. RESULTS: For the first dataset, 100 samples were reserved for testing, and those remaining were for training and validation with a ratio of 90%:10% using K-fold cross-validation. Using the top 10 channels selected by GNMM, we achieved a classification accuracy of 0.80 ± 0.04 for the testing dataset, which compares favourably with results reported in the literature. For the second case, we performed multi-time-windows pre-processing over a single trial. By selecting 6 channels out of 32, we were able to achieve a classification accuracy of about 0.86 for the response correctness classification and 0.82 for the actual responding hand classification, respectively. Furthermore, 139 regression rules were identified after training was completed. CONCLUSIONS: We demonstrate that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier. An important step that affects the effectiveness of GNMM is the pre-processing method. In this paper, we also highlight the importance of choosing an appropriate time window position.

Maintaining an adequate level of security in computer networks is a co-evolving process between improved security techniques and ever more sophisticated attack methods. Our appetite for new technologies shows no abating, evidenced most recently by the smartphone market. Malware continues to be a growing problem and saturation times are becoming so rapid that a continued reliance on signature based protection is becoming impractical as a strategy. We urgently require techniques which enable us to adapt to, and be tolerant of, malicious activity, even if it is an entirely new form of attack, to achieve resilience where otherwise our security fails. Ecology research has found that the impact of disturbances to a community, such as the spread of certain types of viruses, can be reduced by a greater level of biodiversity. There are similarities between dynamic ad hoc networks and natural communities due to their movement and short range communication patterns. We explore here whether biodiversity might offer a security strategy for ad hoc networks.

A genetic algorithm controlled multispot transmitter is demonstrated that is capable of optimising the received power distribution for randomly aligned single element receivers in multiple fully diffuse optical wireless communications systems with multiple mobile users. Using a genetic algorithm to control the intensity of individual diffusion spots, system deployment environment changes, user movement and user alignment can be compensating for, with negligible impact on the bandwidth and root mean square delay spread. It is shown that the dynamic range, referenced against the peak received power, can be reduced up to 27% for empty environments and up to 26% when the users are moving. Furthermore, the effect of user movement, that can perturb the channel up to 8%, can be reduced to within 5% of the optimised case. Compared to alternative bespoke designs that are capable of mitigating optical wireless channel drawbacks, this method provides the possibility of cost-effectiveness for mass-produced receivers in applications where end-user friendliness and mobility are paramount.

The network coding problem (NCP) is an NP-hard combinatorial problem, and genetic algorithms (GAs) have recently been applied to address this problem. This paper reports a novel ripple-spreading GA (RSGA) for the NCP. In contrast to existing GAs where a chromosome directly represents a solution, the proposed RSGA separates chromosomes and solutions by introducing a purpose-designed pre-problem for the NCP. In the pre-problem, the nodes in the NCP are projected into an artificial space, in which some ripple epicenters are randomly generated. Then a specially parameterized ripple-spreading process is employed such that as ripples (starting from the epicenters) spread out in the artificial space, the incoming signals and outgoing signals of all nodes will be individually determined, according to the amplitudes of the ripples which have reached the node. Changing the values of the ripple-spreading parameters will result in different information flows in the networks. Therefore, a simple binary-string based GA, unlike existing GAs which employ permutation representations for the NCP, can be used to optimize the values of the ripple-spreading parameters, in order to find a good solution to the NCP. A potential advantage of the RSGA is its scalability in complex networks, where permutation representation based GAs may face serious memory-efficiency problems. The effectiveness of the proposed RSGA is illustrated in the context of some experiments.

Sensor arrays also known as Electronic Noses (ENs) have been used to analyse the Volatile Organic Compounds (VOCs) of both healthy and infected tomato (Solanum lycopersicum) crops. Statistical and intelligent systems techniques were employed to process the data collected by an EN. Principal Component Analysis (PCA), K-Means clustering and Fuzzy C-Mean (FCM) clustering were applied to visualise any clusters within the dataset. Furthermore, Multi-Layer Perceptron (MLP), Learning Vector Quantization (LVQ) and Radial Basis Function (RBF) based Artificial Neural Network (ANNs) were used to learn to classify and hence categorise the datasets. Using the RBF, MLP and LVQ techniques we achieved 94, 96 and 98% classification accuracy for the healthy, powdery mildew (Oidium lycopersicum) and spider mite infected plants respectively. From these results it is evident that EN is capable of discriminating between the healthy and artificially infected tomato plants and hence may be deployed as a potential early disease detection tool for tomato crops in commercial greenhouses.

Generalized ultra-wide bandwidth (UWB) transmitted reference (TR) receivers proposed previously in the literature are improved with respect to the energy allocation in the data packet and the integration interval length in the correlator. The improvement refers to a best-effort technique to reduce the receiver bit error rate. Simulation results show that the improvement can provide a performance gain of up to 4.2 dB in signal-to-noise ratio. These results give useful guidance on the design of generalized UWB TR receivers.

The optimization of network coding is a relatively new area for evolutionary algorithms, as very few efforts have so far been reported. This paper is concerned with the design of an effective genetic algorithm (GA) for tackling the network coding problem (NCP). Differing from previous relevant works, the proposed GA is designed based on a permutation representation, which not only allows each chromosome to record a specific network protocol and coding scheme, but also makes it easy to integrate useful problem-specific heuristic rules into the algorithm. In the new GA, a more general fitness function is proposed, which, besides considering the minimization of network coding resources, also takes into account the maximization of the rate actually achieved. This new fitness function makes the proposed GA more suitable for the case of dynamic network coding, where any link could be cut off at any time, and consequently, the target rate might become unachievable even if all nodes allow coding. Based on the new representation and fitness function, other GA related techniques are modified and employed accordingly and carefully. Comparative experiments show that the proposed GA clearly outperforms previous methods.

A novel class of optical signature codes based on combinatorial designs is proposed for optical spectral-amplitude code-division multiple-access (CDMA) systems. It is applicable to both synchronous and asynchronous incoherent optical CDMA and is compatible with both frequency-encoded and time-spreading schemes. Simplicity of construction, larger code cardinality and larger flexibility in cross-correlation (CC) control make the proposed code family an interesting candidate for future optical CDMA applications that require a large number of simultaneous users. It has been shown that the system performance can be significantly improved by using the proposed codes with ideal in-phase CC in preference to Hadamard codes.

Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters of such signals, say the frequency. But in previous publications where the frequency is employed to differentiate between events, only one frequency is considered and this frequency was not enough to thoroughly describe the behavior of the composite material. So we introduced the second frequency. A Fast Fourier Transform (FFT) is then applied to the signals resulting from the two frequencies to discriminate different failure mechanisms. This was achieved by using self-organizing map and Fuzzy C-means to cluster the AE data. The result shows that the two approaches have been very successful.

An overview of the developments in optical wireless systems viewed from the traditional communications viewpoint of transmitter, channel and receiver is presented. The trends in modulation formats that match information to the optical wireless channel are considered. This is followed by the discussion of recent transmitter and receiver innovations, particularly the utilisation of diversity transceivers. As a preliminary to the following treatment, the nature and modelling of the optical wireless channel are introduced, with particular emphasis on its unique features in terms of transmitted power constraints and non-negativity. From the examination of modulation formats, on-off‐keying remains the format of choice for basic binary transmission, whereas pulse-position modulation and its derivatives are preferred for more sophisticated requirements. The recent introduction of techniques from radio systems employing subcarriers is seen to be the most promising development in modulation techniques at present. In receiver technology, quasi-diffuse systems employing multispot diffusion and angular diversity are significant developments. They offer lower path loss and less multipath dispersion, at a lower transmission power compared to ‘conventional’ wide-angle diffuse systems, while providing a high level of user mobility compared to line-of-sight transmission. These developments are helping optical wireless systems to fulfil their promise by adopting a philosophy inspired by the radio domain to accommodate operation within a hostile channel.

The important role of quality of service (QoS) in deployment of a resilient dense wavelength division multiplexing (DWDM) backbone for global networks requires critical design-phase planning optimisation. The design issues of resilient DWDM networks for bandwidth and delay sensitive applications of dedicated path protection are addressed. A genetic algorithm (GA) model has been developed to solve the routing and wavelength assignment problem using binary variable-length chromosome encoding under two different schemes of bandwidth optimisation (BOS) and delay optimisation (DOS).The performance of the new GA-based resiliency model has been evaluated for four benchmark networks: PAN EUROPEAN, COST239, NSFNET and ARPA2. Simulation results show a superior capability and efficiency for the model to solve this complex, multi-constraint and nondeterministic polynomial-hard problem for BOS and DOS. The nonlinear nature of this process reveals a significant sensitivity for optical layer network topology on the optimum-design QoS. The results also demonstrate that the PAN EUROPEAN network shows the highest flexibility for primary path design, NSFNET for the secondary path and ARPA2 comes with the lowest design flexibility for both primary and secondary paths.

Performance of optical wireless communication (OWC) systems can be optimised using constant optical power transmission in conjunction with forward error control coding. A new family of constant power error correcting codes using multiple pulse position modulation optical transmission are introduced. This doubly featured technique may extend the capability of many difficult wireless optical links beyond their existing barriers and may enable a higher error performance for OWC networks. The potential and design of look-up table codes are investigated. The Monte Carlo methods provide supportive data for the performance and code word search.

Abstract Managing patient care records has become an increasingly complex issue with the widespread use of advanced technologies. The vast amount of information for every routine care procedure must be securely processed within different databases. Clinical information systems (CIS) address the need for a computerized approach in managing personal health information. Hospitals and public or private health insurance organizations are continuously upgrading their database and data management systems to more sophisticated architectures. The possible support of today's large patient archives and the flexibility of a CIS in providing up‐to‐date patient information and worldwide doctors' collaboration, has leveraged research on CIS in both the academic and the government domains. At the same time, it has become apparent that patients require more control over their clinical data, these being either the results of clinical examinations or medical histories. Due to the large amount of information that can be found on the Internet and the free access to medical practitioners and hospitals worldwide, patients may choose to communicate their information so as to obtain several expert opinions regarding their conditions. Given the sensitive nature of the information stored and inevitably in transit, security has become an issue of outmost necessity. Numerous EU and US research projects have been launched to address security in CIS (e.g., EUROMED, ISHTAR, and RESHEN), whereas regulatory compliance to acts such as the HIPAA has become an obligation for centers moving to CIS. Copyright © 2008 John Wiley & Sons, Ltd.

This paper aims to illustrate for the first time the applicability of emergence within the design, modeling and simulation of wireless routing protocols for the security of information exchange in the presence of a malicious node. Our methodology focuses on the black hole problem and delivers a set of results that describe a protocol prototype within a number of set characteristic pairs. For this purpose, and motivated by the ever growing number of protocol implementations, we define an adaptive system that, through a set of simple rules, has the capability of selecting the fittest structure within a well defined environment capable of dealing with the problem under question.