Forward error correction for molecular communications

Communication between nanoscale devices is an area of considerable importance as it is essential that future devices be able to form nanonetworks and realise their full potential. Molecular communication is a method based on diffusion, inspired by biological systems and useful over transmission distances in the nm to μm range. The propagation of messenger molecules via diffusion implies that there is thus a probability that they can either arrive outside of their required time slot or ultimately, not arrive at all. Therefore, in this paper, the use of a error correcting codes is considered as a method of enhancing the performance of future nanonetworks. Using a simple block code, it is shown that it is possible to deliver a coding gain of ∼1.7 dB at transmission distances of . Nevertheless, energy is required for the coding and decoding and as such this paper also considers the code in this context. It is shown that these simple error correction codes can deliver a benefit in terms of energy usage for transmission distances of upwards of for receivers of a radius

Underwater optical wireless communications : depth dependent variations in attenuation

Depth variations in the attenuation coefficient for light in the ocean were calculated using a one-parameter model based on the chlorophyll-a concentration Cc and experimentally-determined Gaussian chlorophyll-depth profiles. The depth profiles were related to surface chlorophyll levels for the range 0–4  mg/m2, representing clear, open ocean. The depth where Cc became negligible was calculated to be shallower for places of high surface chlorophyll; 111.5 m for surface chlorophyll 0.8<Cc<2.2  mg/m3 compared with 415.5 m for surface Cc<0.04  mg/m3. Below this depth is the absolute minimum attenuation for underwater ocean communication links, calculated to be 0.0092  m−1 at a wavelength of 430 nm. By combining this with satellite surface-chlorophyll data, it is possible to quantify the attenuation between any two locations in the ocean, with applications for low-noise or secure underwater communications and vertical links from the ocean surface

An effective genetic algorithm for network coding

The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm

Multi-user indoor optical wireless communication system channel control using a genetic algorithm

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

Epidemic modelling by ripple-spreading network and genetic algorithm

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results

PaFiR : Particle Filter Routing – a predictive relaying scheme for UAV-assisted IoT communications in future innovated networks

Increasing urbanization, smart cities and other cutting-edge technologies offer the prospect of providing more functions to benefit citizens by relying on the substantial data processing and exchange capabilities now possible. This can generate significant unpredictable and unbalanced data loads for the bearing IoT network to support its application and service demands. We thus propose a wireless routing scheme designed to use the Particle Filter algorithm to empower portable smart devices with intelligent capacities for the radio communication system. This facilitates the offloading of traffic from traditional wireless networks and enables the IoT system to adopt unmanned aerial vehicles, thus also offering further innovation to flying network platforms. The proposed PaFiR routing protocol offers the network more scalability, tolerance and resilience, to achieve the goal of smart relaying. Simulation results that demonstrate the routing algorithm designed offers excellent performance when compared with existing wireless relaying schemes. It provides delivery ratios that are improved by up to 40% without unmanageable increases in latency or overheads

Non-coherent detection for ultraviolet communications with inter-symbol interference

Ultraviolet communication (UVC) serves as a promising supplement to share the responsibility for the overloads in conventional wireless communication systems. One challenge for UVC lies in inter-symbol-interference (ISI), which combined with the ambient noise, contaminates the received signals and thereby deteriorates the communication accuracy. Existing coherent signal detection schemes (e.g. maximum likelihood sequence detection, MLSD) require channel state information (CSI) to compensate the channel ISI effect, thereby falling into either a long overhead and large computational complexity, or poor CSI acquisition that further hinders the detection performance. Non-coherent schemes for UVC, although capable of reducing the complexity, cannot provide high detection accuracy in the face of ISI. In this work, we propose a novel non-coherent paradigm via the exploration of the UV signal features that are insensitive to the ISI. By optimally weighting and combining the extracted features to minimize the bit error rate (BER), the optimally-weighted non-coherent detection (OWNCD) is proposed, which converts the signal detection with ISI into a binary detection framework with a heuristic decision threshold. As such, the proposed OWNCD avoids the complex CSI estimation and guarantees the detection accuracy. Compared to the state-of-the-art MLSD in the cases of static and time-varying CSI, the proposed OWNCD can gain ∼1 dB and 8 dB in signal-to-noise-ratio (SNR) at the 7% overhead FEC limit (BER of 4.5×10 −3 , respectively, and can also reduce the computational complexity by 4 order of magnitud

Impulse response modeling for underwater optical wireless channels

In underwater optical wireless communication (UOWC) channels, impulse response is widely used to describe the temporal dispersion of the received signals. In this paper, we propose a new function to model the impulse response in most realistic cases in UOWC channels. By exploiting the inherent properties of such channels, our newly proposed model is superior to the conventional weighted double gamma functions (WDGF) model in explaining the behavior of the channel. We use Monte Carlo simulation to verify that our newly proposed model has a better accuracy of numerical fitting in most cases. Therefore, this new modeling approach offers a more convenient way to evaluate the performance of different kinds of UOWC channels

Energy conscious adaptive security scheme : a reliability-based stochastic approach

The increasing importance of information and communication, which plays a big role in a number of different fields in the modern era, brings with it the need for security. At the same time, encryption, which is an indispensable part of security architecture, is computationally intensive and may require a significant amount of energy consumption. Thus, it is of great importance to provide a sufficient level of security while properly utilising the available resources. This research suggests a security framework based on the Reliability Function, along with the added ability to dynamically adjust the security level with respect to energy consumption, either according to the severity of the requested service or according to a specified energy threshold

Simulating the performance of SW-ARQ schemes within molecular communications

This paper provides results on an investigation concerning the application of five tailored Stop-and-Wait Automatic Repeat reQuest (SW-ARQ) schemes to a diffusion based molecular communication system. Each scheme is numerically simulated and evaluated to determine its performance with regards to average time cost and energy consumption. It is shown that all five schemes are beneficial depending upon the application scenario. Scheme 1 is the best choice for adjacent communications although, if a slightly higher energy budget can be afforded, schemes 2 and 3 will provide better performance than scheme 1 as the communication distance increases. Schemes 4 and 5 are designed to benefit scenarios with either a varying channel or for a channel with unknown parameters although will also benefit a static channel if again, further system energy can be utilised. This optimisation and trade-off between time and energy requirement for a complete successful transmission will become more important in future applications involving molecular communications where energy efficiency is a design consideration