This study presents a simulated transceiver with a microstrip patch antenna (MPA) designed to resonate at 150 GHz and embedded in paint. The in-paint MPA (IP-MPA) is designed for the Internet of Paint (IoP) paradigm, which envisions seamless device communication through a paint layer on walls. This study introduces a comprehensive channel model for transceivers in paint at arbitrary depths and IP-MPA orientations. The best antenna orientations are analyzed for IoP channel performance. Extensive simulations indicate that the lateral waves, which propagate along the air-paint interface, exhibit the lowest loss, making this path the most reliable for communication between transceivers in paint. Furthermore, the maximum received power for each propagation path, except for the direct path, depends on depth. The findings suggest that the proposed network of IP-MPA-enabled transceivers for IoP has the potential to transform conventional walls into an integrated high-speed wireless communication and sensing infrastructure.

This work opens a new chapter in the 100, 000 year-old concept of paint, by leveraging innovations in nano-technology in the sub-THz frequency range. More specifically, the groundbreaking concept of Internet of Paint (IoP) is introduced along with a comprehensive channel model and a capacity analysis for nano-scale radios embedded in paint and communicating through paint. Nano-network devices, integrated within a paint medium, communicate via a multipath strategy, encompassing direct waves, reflections from interfaces, and lateral wave propagation. The evaluation incorporates three distinct paint types to assess path losses, received powers, and channel capacity. Analysis of path loss indicates a slight non-linear increase with both frequency and Line of Sight (LoS) distance between transceivers. Notably, paints with high refractive indexes result in the highest path loss. Moreover, burying transceivers at similar depths near the Air-Paint interface showcases promising performance of lateral waves with increasing LoS distance. Increasing paint layer depth leads to amplified attenuation, while total received power exhibits promising results when in close proximity to the Air-Paint interface but steeply declines with burial depth. Additionally, a substantial reduction in channel capacity is observed with LoS distance and burial depth, so transceivers need to be close together and in proximity of the A-P interface to communicate effectively. Comparing paint and air mediums, IoP demonstrates approximately two orders of magnitude reduction in channel capacity compared to air-based communication channels. This paper provides valuable insights into the potential of IoP communication within paint mediums and offers a foundation for further advancements in this emerging field.

Bacterial ecosystems are integrated with cascading molecular communications networks that contain redundant paths transmitting molecular signals through a shared medium, resulting in accumulation of diverse molecules. Due to a range of factors, including residual noise and channel attenuation, the information flow between bacterial populations can be affected. Although the cooperative transceiver bacterial populations in parallel paths of the network amplify molecular signals to overcome channel attenuation, it further minimises the residual noise by absorbing higher signal molecules resulting in reliable information flow through the network. In this study, using information and molecular communications theory, we investigate the impact of Cooperative Amplification (CA) on InterSymbol Interference (ISI) in Bacterial Molecular Communication Networks (BMCN) with redundant paths. Moreover, we analyse the information flow through a cascading and parallel molecular communications system that uses different molecules as signals. We first show the effect of CA on the ISI and then the reliability of bacterial molecular networks using a vital metabolic functionality of the Human Gut Bacteriome (HGB), which is Short Chain Fatty Acids (SCFA) production. The analysis on the CA shows that the performance of the network can be enhanced up to a certain level by increasing the number of cooperate transceivers. Finally, the estimated Mutual Information (MI) for each bacterial population for three different networks using the data generated from simulations, indicates that the molecular communication network with redundant paths can support reliable information flow despite significant molecular residual noise.

The COVID-19 pandemic has resulted in one of the major challenges for humanity in the 21st century. The impact of these challenges has led to a tremendous loss of life, impact on long-term health, well-being as well as personal psychology, and negative societal changes and not to mention its impact on the global economy. Since this is a health issue, similar to other forms of diseases and pandemics, society has largely relied on the fields of medical, virology, immunology, biotechnology, and pharmaceutical science to develop novel therapeutic solutions for treatments. This has resulted in vaccines that have been rolled out to elevate immunity levels that will hopefully allow the majority of the population to reach herd immunity. However, given the technological advancements that we have reached in the 21st century, questions have also risen as to how other disciplines can play a role in solving and obtaining new knowledge of communicable disease pandemics.

Alterations in the human Gut Bacteriome (GB) can be associated with human health issues, such as type-2 diabetes and obesity. Both external and internal factors can drive changes in the composition and in interactions of the human GB, impacting negatively on the host cells. This paper focuses on the human GB metabolism and proposes a two-layer network system to investigate its dynamics. Furthermore, we develop an in-silico simulation model (virtual GB), allowing us to study the impact of the metabolite exchange through molecular communications in the human GB network system. Our results show that regulation of molecular inputs strongly affects bacterial population growth and creates an unbalanced network, as shown by shifts in the node weights based on the produced molecular signals. Additionally, we show that the metabolite molecular communication production is greatly affected when directly manipulating the composition of the human GB network in the virtual GB. These results indicate that our human GB interaction model can help to identify hidden behaviours of the human GB depending on molecular signal interactions. Moreover, the virtual GB can support the research and development of novel medical treatments based on the accurate control of bacterial population growth and exchange of metabolites.

In this paper we consider the influence of inter-cellular communication on the development and progression of Glioblastoma Multiforme (GBM), a grade IV malignant glioma which is defined by an interplay Grow i.e. self renewal and Go i.e. invasiveness potential of multiple malignant glioma stem cells. Firstly, we performed wet lab experiments with U87 malignant glioma cells to study the node-stem growth pattern of GBM. Next we develop a model accounting for the structural influence of multiple transmitter and receiver glioma stem cells resulting in the node-stem growth structure of GBM tumour. By using information theory we study different properties associated with this communication model to show that the growth of GBM in a particular direction (node to stem) is related to an increase in mutual information. We further show that information flow between glioblastoma cells for different levels of invasiveness vary at different points between node and stem. These findings are expected to contribute significantly in the design of future therapeutic mechanisms for GBM.

Glioblastoma Multiform (GBM) is known as one of the most malignant tumours in the brain, and challenges remain in developing effective therapeutic solutions. This paper addresses an open-loop control molecular communication system using an adaptive algorithm that controls engineered induced Neural Stem Cells (iNSCs) to release therapeutic exosomes for treating GBM. The adaptive algorithm is based on the Lotka-Volterra Predator-Prey model, and virtually monitors the tumour growth from an external Brain-Machine Interface to control the release of the exosomes for the treatment. We developed the model to incorporate the control from an external RF signal that controls the production of exosomes as well as the diffusion propagation of exosomes through a 3D simulated Extracellular Space tissue. Based on numerical analysis coupled with simulations, we found that factors such as stochastic propagation of exosomes influence the aggressiveness of the model to tackle the tumour. This work can lay the foundation for future adaptive Brain-Machine Interface that controls molecular communication system for GBM treatment.

Synthetic biology has utilised engineering concepts for the rational design of biocompatible systems. Here we utilise two bacterial populations to create a modulated molecular channel coding scheme for molecular communications systems. We believe this approach can drive the development of more reliable biocompatible molecular communications systems to apply in the dairy industry.

The functioning of bacterial nanonetworks as a ”drug delivery system” requires the engineered bacteria to track the targets, such as harmful micro-organisms, pathogens, or chemical weapons, to release drug molecules effectively. The coordinated and intelligent movement of energy-constrained engineered bacteria is desired for successful tracking of mobile targets. In this work, first, we analyze the energy consumption by engineered bacteria for releasing molecules and propagating for the tracking process. Then we show that the events of the release of molecules by engineered bacteria and their propagation are interlinked in such a way that the strategy of releasing attractants upon detecting the target is coupled to the energy available with the engineered bacteria. Based on the finding, we propose an energy-aware algorithm, named as EnPoS, which probabilistically selects a group of engineered bacteria among the deployed bacterial population to release signaling molecules over a particular time period in order for engineered bacteria to track the mobile targets. The simulation results show better performance of the proposed algorithm as compared with the basic algorithm incorporating continuous releasing of signaling molecules, concerning the energy expenses, mean displacement over time, and distribution of the engineered bacteria around the targets.

Recent Brain-Machine Interfaces have moved towards miniature devices that can be seamlessly integrated into the cortex. In this paper, we propose communication between miniature devices using light. A number of challenges exist using nanoscale light-based communication and this includes diffraction, scattering, and absorption, where these properties result from the tissue medium as well as the cell's geometry. Under these effects, the paper analyses the propagation path loss and geometrical gain, channel impulse and frequency response through a line of neurons with different shapes. Our study found that the light attenuation depends on the propagation path loss and geometrical gain, while the channel response is highly dependent on the quantity of cells along the path. Additionally, the optical properties of the medium impact the time delay at the receiver and the width and the location of the detectors. Simulations were conducted for cells that are lined horizontally up to a distance of 450 μm using light wavelength of 456 nm and different neuron densities (men's neocortex (25924(±15110) /mm3) and women's (27589(±16854) /mm3)). Based on the simulations, we found that spherical cells attenuate approximately 20% of the transmitted power compared to the fusiform and pyramidal cells (35% and 65%, respectively).

The birth of wireless communication systems nearly 1 century ago has transformed and redefined the way humans communicate and interact. This transformation, which has opened boundaries between societies and eliminated cultural barriers, has evolved from the original paradigm of wireless electromagnetic communication systems. This paradigm has experienced numerous evolutionary developments that have not only brought along seamless connectivity for human interactions but also communication between machines and devices.

The papers in this special issue aims at capturing the most relevant theoretical foundations of molecular communications and networking (MCN) established so far, while giving a timely perspective on some emerging technological directions for engineering practical MCN applications. We believe these special issue papers can serve as an essential but complete handbook to approach MC as a research topic, as well as to start contributing to its timely and much needed technological evolution from theoretical exploration to practical implementation.

Over the past 10 years, molecular communication (MC) has established itself as a key transformative paradigm in communication theory. Inspired by chemical communications in biological systems, the focus of this discipline is on the modeling, characterization, and engineering of information transmission through molecule exchange, with immediate applications in biotechnology, medicine, ecology, and defense, among others. Despite a plethora of diverse contributions, which has been published on the subject by the research community, a general framework to study the performance of MC systems is currently missing. This paper aims at filling this gap by providing an analysis of the physical processes underlying MC, along with their information-theoretic underpinnings. In particular, a mathematical framework is proposed to define the main functional blocks in MC, supported by general models from chemical kinetics and statistical mechanics. In this framework, the Langevin equation is utilized as a unifying modeling tool for molecule propagation in MC systems, and as the core of a methodology to determine the information capacity. Diverse MC systems are classified on the basis of the processes underlying molecule propagation, and their contribution in the Langevin equation. The classifications and the systems under each category are as follows: random walk (calcium signaling, neuron communication, and bacterial quorum sensing), drifted random walk (cardiovascular system, microfluidic systems, and pheromone communication), and active transport (molecular motors and bacterial chemotaxis). For each of these categories, a general information capacity expression is derived under simplifying assumptions and subsequently discussed in light of the specific functional blocks of more complex MC systems. Finally, in light of the proposed framework, a roadmap is envisioned for the future of MC as a discipline.

The birth of wireless communication systems nearly a century ago has transformed and redefined the way humans communicate and interact. This transformation has evolved over many years and has brought along not only seamless connectivity for human interactions but also communication between machines and devices. While these communication systems are manmade artifacts, the research community has more recently turned its attention to other communication strategies that have spontaneously evolved in nature.

Artificial neural stimulation of a peripheral nerve can create an in-body data communications channel. We propose the stimulation of a peripheral nerve using energy-harvesting arrays of nanodevices, embedded in biocompatible tissue patches. The resulting extracellular compound action potential (CAP) pulse can provide a data bit-stream for communicating with an embedded receiver. Our objective is to determine the maximum achievable transmission range of a CAP along a nerve and the maximum sustainable bit rate. We model the generation of a CAP and then compute the reduction in amplitude and the spreading of the pulse with propagation distance. The channel capacity is calculated for ON-OFF keying (OOK) and digital pulse interval modulation. We show that the transmission range depends on the number and diameters of the activated neurons contributing to the CAP amplitude and width. Our modulation analysis demonstrates the effects of attenuation, background noise, the neural refractory period, and pulse broadening on the achievable bit rate. We show how a maximum OOK bit rate of 200 bit/s can be sustained over transmission distances greater than 100 mm. The proposed approach provides a low bit rate, unidirectional asynchronous transmission system that could, for example, deliver simple instructions to an embedded drug-delivery system.

Social network analysis (SNA) is a technique to study behavioral dynamics within a social group. In SNA, it is an open question whether it is possible to characterize animal-level behaviors by using group-level information. Also, it was believed that the combined use of SNA would provide a more comprehensive understanding of social dynamics. In light of these two factors, here we explain an approach to evaluate animal importance to a group by considering the variability in group-level structural information, which is computed by joining the animal- and group-level SNA measures node centrality and network entropy, respectively. Moreover, two other metrics, animal social interaction range and nearest-neighbor frequency matrix, which represent a social affiliation of each animal within the group, are computed to help address the general challenges in graph-based SNA and, thereby, improve the precision of animal importance measures. Finally, we derive the joint distribution of animal importance of the group in detecting atypical social behaviors. The approach is tested using tracking data of dairy cows. The reliability of the derived animal importance was superior to the already existing animal importance measures. To illustrate the usability of the animal importance metric, a simulation study was conducted to identify sick and estrus animals in a group. The social affiliation of sick cows was less when compared to healthy cows. Also, their individual distributions of animal importance were shifted toward the left of the mean of the animal importance distributions of healthy cows. Consequently, the joint distribution of animal importance of the group exhibited a bimodal distribution with a left tailored shape. The behavior of cows in estrus was opposite to that of sick cows. Moreover, with the increasing number of sick and estrus cows in the group, respectively, the group entropy decreased with larger variance and slightly increased with less variance. Therefore, the entropy-based animal importance metric has superior performances when evaluating animal importance to the group compared to the existing metrics. It can be used for generating alerts for the early detection of atypical social behaviors associated with, for instance, animal health, veterinary, and welfare.

The outbreak of the Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing the Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60 percent of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into a future treatment system whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.

In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for Deep Brain Stimulation (DBS). However, there are limitations and challenges with the current technologies. Firstly, the stimulation of neurons currently is only possible through implantable electrodes which target a population of neurons. This results in challenges in the event that stimulation at the single neuron level is required. Secondly, a major hurdle still lies in developing miniature devices that can last for a lifetime in the patient's brain. Recently, the concept of neural dust has been introduced as a way to achieve single neuron monitoring and potentially actuation. In parallel to this, the field of optogenetics has emerged where the aim is to stimulate neurons using light, usually by means of optical fibers inserted through the skull. Obviously, this introduces many challenges in terms of user friendliness and biocompatibility. We address this shortcoming by proposing the wireless optogenetic neural dust (wi-opt neural dust). The wiopt neural dust is equipped with a miniature LED that is able to stimulate the genetically engineered neurons, and at the same time harvest energy from ultrasonic vibrations. The simulation results presented in the paper investigates the behaviour of the light propagation in the brain tissue, as well as the performance of designed circuitry for the energy harvesting process. The results demonstrates the feasibility of utilizing wi-opt neural dust for long term implantation in the brain, and a new direction towards precise stimulation of neurons in the cortex.

The ICT industry has come under criticism as being one of the major energy consumers to exacerbate high global carbon emissions. Meanwhile, using renewable energy to power ICT infrastructure is becoming an attractive solution and is gaining its momentum due to the recent breakthroughs of converting solar and wind energies as power sources at competitive costs. Although significant amounts of fossil fuel based-energy can be saved by allowing network devices (e.g., routers and line-cards) to be set to sleep, this optimization approach comes at a price of degrading routing performance, i.e., the quality of service. This paper addresses the problem of minimizing fossil fuel consumption in large Internet Service Provider (ISP) networks, by utilizing a novel gradient-based routing protocol, which favors forwarding packets along routers powered by the highest quantity of renewable energies. Besides favoring renewable energy, the proposed routing protocol can support putting routers to sleep in order to optimize energy consumption while ensuring a minimum degradation in routing performance. Through our evaluation utilizing real meteorological data, our proposed solution has demonstrated a massive reduction of fossil fuel usage by the network (> 70%) while maintaining the routing performance to a similar level when no energy optimization is applied.

Utilizing bacteria as information carriers is a promising technique for molecular communication to establish connections and networking capabilities between micro- and nanoscale devices. A particular process, that is of interest in this paper, and vital to achieve reliable networking performance in bacterial nanonetwork, is conjugation. Conjugation is a process where bacteria come within close range to form physical connections to allow plasmids to be transferred. However, there are a number of processes that could cause loss or damage during the transfer process, such as external vibrations. Message encoding techniques are envisioned as a promising technique to mitigate these effects. In this study we first define the concept of optimal message encoding for bacterial nanonetworks providing the upper bound on the message delivery time and the probability of message delivery within the specified time. We then investigate the effect of encoding on the performance of these metrics by proposing and numerically comparing several feasible encoding techniques. The performance comparison has been done using a specifically developed simulation environment capturing bacteria movement and interactions. Numerical results demonstrate that even the simple encoding strategies allow to significantly improve the performance compared to the baseline system.

The six papers in this special section focus on the Internet of nanotechnology things. While researchers are currently investigating these challenges to develop fully functional nano communication systems, a question remains as to whether they can represent an extended communication network that is part of the broader Internet. These papers address new solutions for the Internet of Nano Things. The Internet of Things paradigm has transformed the way we operate our personal and professional lives, it is driving our economy and will continue to enable many new opportunities in broad research areas. As this pervasive and ubiquitous interconnection of our everyday life appliances continues into the future, new types of devices enabled by nano and biotechnology promise to push engineering to previously unexplored application domains, where the exchange of information and access from/to the broader Internet for their monitoring and control are even more essential. The research on nanoscale communication and networks aims to develop systems for interconnecting these novel devices at the nanoscale, i.e., the Internet of Nano Things.

The emergence of molecular communication has provided an avenue for developing biological nanonetworks. Synthetic biology is a platform that enables reprogramming cells, which we refer to as Bio-NanoThings, that can be assembled to create nanonetworks. In this paper, we focus on specific Bio-NanoThings, i.e, bacteria, where engineering their ability to emit or sense molecules can result in functionalities, such as cooperative target localization. Although this opens opportunities, e.g., for novel healthcare applications of the future, this can also lead to new problems, such as a new form of bioterrorism. In this paper, we investigate the disruptions that malicious Bio-NanoThings (M-BNTs) can create for molecular nanonetworks. In particular, we introduce two types of attacks: blackhole and sentry attacks. In blackhole attack M-BNTs emit attractant chemicals to draw-in the legitimate Bio-NanoThings (L-BNTs) from searching for their target, while in the sentry attack, the M-BNTs emit repellents to disperse the L-BNTs from reaching their target. We also present a countermeasure that L-BNTs can take to be resilient to the attacks, where we consider two forms of decision processes that includes Bayes' rule as well as a simple threshold approach. We run a thorough set of simulations to assess the effectiveness of the proposed attacks as well as the proposed countermeasure. Our results show that the attacks can significantly hinder the regular behavior of Bio-NanoThings, while the countermeasures are effective for protecting against such attacks.

Calcium (Ca2+)-signaling-based molecular communication is a short-range communication process that diffuses and propagates ions between the cells of a tissue. The communication process is initiated via stimulation and amplification of the production of Ca2+ions within a cell; these ions then diffuse through a physical connection between cells called a gap junction. Ca2+signaling can be found in different classes of cell. In excitable cells, initiation of the Ca2+-signaling process is accompanied by an electrical component; for nonexcitable cell types, the electrical component is absent; while hybrid cells exhibit both behaviors. This paper provides a comparison and analysis of the communication behavior in tissues comprised three specific cell types that utilize Ca2+signaling: epithelium cells (nonexcitable), smooth muscle cells (excitable), and astrocytes (hybrid). The analysis focuses on spatiotemporal Ca2+concentration dynamics and how they are influenced by the intracellular signaling process, the molecular diffusion delay, the gain and capacity of the communication channel, as well as intracellular signaling interference. This analysis of the communication behavior in the context of tissues provides insights useful for, inter alia, the design of nanomachines that are situated within tissues and that use analysis of the communication channel to infer tissue health.

We propose and evaluate a number of of frequency selection strategies for nano-scale devices using graphene-based nano-antennas (“graphennas”), which operate in the Terahertz band. The strategies take into account the limitations of Terahertz channel and aim to optimize the overall network transmission rate of a network of nano-devices, while maximizing various objectives. We investigate the trade-off between cases where: 1) frequency duplication within the network is allowed or prevented; 2) limiting the spread of frequencies over the entire Terahertz range is required; and 3) balancing the load between the network sink nodes is required. We compare the network performance for the different objectives proposed against a random frequency selection strategy. Our simulation study demonstrates the efficiency of the proposed algorithms and indicates their usefulness in different application scenarios.

One form of molecular communications for short range transmission between nanomachines is Calcium Signaling. This form of signaling is commonly found in cellular tissues, which consist of tightly packed cells, whereby Ca2+ions propagate and diffuse between the cells. However, the natural flexible structure of cells usually leads to them dynamically changing shapes under certain strains and forces. Since the interconnected cells form a tissue, changes in the shape of one cell will change the shape of neighboring cells and the tissue as a whole. This may in turn significantly impair the communication channel between the nanomachines (which we assume to be embedded within the cells). In order to counter this problem, we propose an adaptive transmission protocol for Ca2+signaling based molecular communications in cellular tissues. The protocol operates in two phases. The first phase utilizes information metrics to infer the state of the tissue; second phase then involves the determination of the most appropriate time-slot for bit transmission. In this way, we aim to improve the information rate by using a time slot length that is appropriate for the prevailing type of tissue deformation. Through simulation studies we show that, for two types of deformation and two different topologies, our protocol can improve the information rate performance by 15%.

This paper proposes an improved version of a fully distributed routing protocol, that is applicable for cloud computing infrastructure. Simulation results shows the protocol is ideal for discovering cloud services in a scalable manner with minimum latency.

While many aspects of the Future Internet are uncertain, one thing that is clear is that service demand will continue to rise. Also, advances in mobile devices and service technology will almost certainly cause service usage patterns to vary considerably. Another issue that the Future Internet community must be acutely aware of is the huge movement towards more sustainable forms of computing and communications technology. With the recent attention that has been put on IT energy consumption (data-centres in particular), all computing and communications systems need to consider their environmental impact from the outset. With that in mind, we propose a solution for determining the optimal placement of services in data-centre network, in order to maximize the overall renewable energy usage and minimize the cooling energy consumption. We then perform a series of experiments in order to evaluate our solution, incorporating dynamic service request profiles and actual weather and renewable energy production values.

The emergence of new platforms providing Internet protocol television (IPTV) services, such as Google TV and Apple TV is expected to revolutionize the way multimedia services are provided on the Internet. However, the existing network infrastructure falls well short of meeting the resource requirements to provide satisfactory levels of such services. This has led the service providers like Google to contemplate deploying large farms of network cables to transport the traffic generated by their IPTV services. However, the huge cost involved in achieving their target, means careful resource planning and meeting users' expectations are essential. In this paper, we propose an effective adaptive HYbrid delivery System Adaptive Content management (HySAC), which dynamically selects the mode of video delivery from unicast, multicast and peer-to-peer (P2P) based on the popularity of the respective videos. The videos are dynamically classified into high, medium and low popularity groups and, they are delivered using multicast, P2P and unicast methods respectively. HySAC reduces the start-up delay as the mode of delivery is well defined and the resource availability to support the delivery is guaranteed. We have evaluated the performance of HySAC on a realistic simulated environment and presented experimental results; they indicate that HySAC performs significantly better than pure unicast, multicast and P2P mechanisms. This paper also provides a mechanism to classify the videos into different popularity classes.

IEEE 802.11 wireless networks have received much attention over the past number of years. Still certain aspects of behavior of wireless networks have not been studied well enough. For example, understanding MAC layer packet delay distribution remains challenging yet. However, obtaining such distribution is highly beneficial for modeling QoS provided by wireless networks. This paper proposes a way of obtaining MAC delay distribution in case of single-hop networks. The proposed way is based on theory of terminating renewal processes and delivers approximation of good precision.

Growing popularity of wireless ad hoc networks leads to higher demands on performance of all TCP/IP stack layers. Usually ad hoc networks operate according to IEEE 802.11 standard which provides a MAC layer protocol that uses a single channel for data transmissions. However, increasing the number of data channels on MAC layer improves performance of ad hoc wireless networks by letting nodes simultaneously transmit data through different channels. Nevertheless network performance improvement will be diminished if routing mechanisms are not efficient and reactive to load changes within the network. In this paper we introduce a multi-channel MAC layer cooperative technique that integrates gradient based routing to support multi-hop wireless transmission. We show that using the gradient based routing to support multi-channel MAC protocols can enhance overall throughput of the network, and improve network load balancing. Simulations have also been conducted to validate the proposed solution.

QoS-aware network planning becomes increasingly important for network operators and ISP alike as the number and heterogeneity of applications supported by networks continue to increase. We have proposed an architecture and methodology to support QoS-aware route planning for IPTV networks. The route planning problem was formulated as a residual bandwidth optimization problem and solved using GA-VNS hybrid algorithm. The QoS guarantee is provided by performing route allocation for a QoS-aware demand matrix which is generated using efficient and accurate estimation of QoS requirements using empirical effective bandwidth estimations. This paper revisits our formerly proposed approach to perform residual bandwidth optimization with QoS guarantee in IPTV networks and proposes alterations and parameter settings to best suit the problem in terms of robustness and performance. This paper proposes the use of two cost functions to meet the above requirements and the use of a variation of the original VNS algorithm besides recommending the use of specific operators and general parameter settings. The proposals and recommendations are substantiated by the results of performance evaluations and analysis. The results indicate dramatic improvement when the proposed approaches are taken on board.

The recent years have seen an explosive upsurge in the popularity of IPTV networks and the IPTV user base. The increased user base poses new challenges to IPTV provider in providing the IPTV services with the expected QoE to the users due to the increase in traffic and heterogeneity in networks. The VoD, being the most prominent and most lucrative of the triple play components, require efficient managing in order for the IPTV provider to meet the expectations of the users, i.e. efficient management of contents. In this paper, we propose PaCRAmby adding a new multicast approach SMoD which combines unicast and multicast, to our earlier proposed content replication strategy PaCRA. SMoD provides satisfactory levels of QoE by eliminating the batching delay which is present in the traditional multicast approaches by exploiting the segmentation of videos. We show that the application of SMoD significantly improves the performance of PaCRA in terms of resource utilization, rejection rate and number of content replications. We further recommend a pre-population strategy for video segments to improve the performance of SMoD by taking into account the segment numbers for future work.

Wireless networks have grown their popularity over the past number of years. Usually wireless networks operate according to IEEE 802.11, which specifies protocols of physical and MAC layers. A number of different studies have been conducted on performance of IEEE 802.11 wireless networks. However, questions of QoS control in such networks have not received sufficient attention from the research community. This paper considers modeling of QoS of IEEE 802.11 networks defined in terms of throughput requirements and packet loss probability limitations. An influence of sizes of packets being transmitted through the network on the QoS is investigated. Extensive simulations confirm results obtained from the mathematical model.

The recent years have seen an explosive upsurge in the popularity of IPTV networks and the IPTV user base. The increased user base poses new challenges to ISP in providing the IPTV services with the expected QoE to the users due to the increase in traffic and heterogeneity in networks. The VoD, being the most prominent and most lucrative of the triple play components, require efficient managing in order for the ISP to meet the expectations of the users, ie. efficient management of contents. The existing content management techniques that have been applied in CDN and P2P networks are not applicable to the VoD system in IPTV due to the difference in network characteristics. The available IPTV content management techniques can only provide sub-optimal performance as they do not take into account the load on the links. In this paper, we propose a new IPTV architecture that supports coordinated route planning and content management using overlays. We present a new distributed content management approach namely PaCRA which intelligently performs content management by taking into account the server storage status, network path status and the content utility. We present experimental evaluations that show that PaCRA performs significantly better than the other standard approaches. We further show that the traditionally used Zipf distribution fails to provide an approximation for content popularity distribution and propose a new content popularity distribution which is found to closely follow the actual video popularity distribution.

The current Internet infrastructure is facing a number of limitations that is not suitable to meet the growing number of services and users. In particular, one aspect that requires enhancement is routing, where original routing concepts were designed for static traffic patterns with minimal variations and supporting mainly low throughput traffic (e.g. Data). As the number of users as well as services supporting the user grows, the current routing mechanisms will not be feasible. In this paper we present a gradient based distributed routing technique that is based on discovering routes through a gradient field created in the topology. The gradient calculation is based on weighted sum of a number of components, which modifies the gradient field as the network load changes. Simulation results have also been presented to validate the proposed routing algorithm.

There is currently much debate in defining what form the future Internet will take. The current Internet is struggling to meet the needs of an ever-evolving society. This is largely due to the Internet now become a thriving marketplace with services at the core. The range, number and complexity of services are set to increase with an even more dynamic service environment envisioned in the future. However, as these services grow, service composition will become an important feature of the service environment, leading to new challenges in service discovery and composition mechanisms. At the same time, dynamic service environments will also require that the underlying infrastructure networks are flexible enough to handle the changing service landscape. One area this is particularly important is in dynamic routing to deal with highly dynamic and frequent service changes. In this paper, we adopt mechanisms from biology and apply these to the problems identified, resulting in an integrated bio-inspired service management and dynamic routing solution for Future Internet. We demonstrate how the bio-inspired mechanisms not only improve each problem individually, but through their integration also improve overall network performance. Simulation results are presented to validate the proposed solution.

QoS-aware network planning becomes increasingly important for large network operators and ISPs as the number and heterogeneity of applications supported by networks continues to increase. This paper proposes a novel approach to performing residual bandwidth optimization with QoS guarantee to support IPTV services on the Internet backbone. The approach combines the use of a new highly scalable hybrid GAVNS algorithm (genetic algorithm with variable neighborhood search) with the efficient and accurate estimation of QoS requirements using empirical effective bandwidth estimations. The QoS requirements for expected traffic are estimated using a relatively small number of traffic samples for different traffic classes and a QoS-aware demand matrix is built based on the calculated effective bandwidth and expected mean rates. This paper further proposes a strategy to perform content replication to mitigate network load caused by VoD (video on demand) traffic component of IPTV traffic. Given such a demand matrix, experimental results indicate that the GA-VNS algorithm shows significantly better performance in comparison to OSPF in terms of link utilization distribution, maximum link utilization, rejection rate and suitability to incorporate content replication. Additional results indicate that GA-VNS algorithm is very effective under dynamic conditions when the knowledge of previous route plan is taken into consideration.

Heart disease is the number one killer in the civilized world, accounting for around 1.9 million people every year in the EU, with the associated annual health costs of approximately EUR 105 billion. Important aspects of treating cardiac problems are monitoring and directing critical data to key

The vision of Autonomic Network Management seeks self-governance (e.g. self-organisation, self-management) capabilities amongst network devices and system to minimise complexities found in current network management. Policy based management system provides a mechanism to counter these complexities, where the system provides a consistent model for decision making process. In this paper we propose bio-inspired mechanisms to support network survivability that is integrated with policy based management. Our solution is based on adaptation and integration of various biological principles such as Blood Glucose Homeostasis, Chemotaxis, Reaction-Diffusion and Hormone Signaling. The bio-inspired principles support a high degree of robustness, which is a key feature towards adapting in fluctuating network environment. Simulations results have also been presented to illustrate our proposed solution.

In this paper we present a biological Nano and MEMS communication platform that uses Nano logic computation based on controlled cell-signaling mechanisms. The proposed solution also integrates compartmentalisation techniques to support parallel processing. Based on our solution, our objective is to create a nanoscale communication networks between various biological Nano and MEMS devices within a tissue to support various medical applications (e.g. patient monitoring).

This paper proposes a unique feedback governed multi layered architectural model to support ISP's traffic demands with multiple quality of service(QoS) constraints. The proposed model consists of different modules each responsible for a particular set of tasks. The most challenging task involved in satisfying the demands is routing the traffic subject to multiple QoS constraints for multiple internet service providers (ISP). Routing the traffic subject to multiple constraints itself is known to be an NP-hard problem. This paper addresses the problem of finding the optimum routes to satisfy the demands of different ISPs, where different ISPs have different demands and their priority of QoS keep changing. A genetic algorithm (GA) which makes use of heuristic technique is proposed in this paper. All the optimum routes are found in one run of the program, therefore the chromosome selected encodes all the demanded routes. This paper also makes use of employing a tournament selection mechanism where the diversity of the population is preserved while the best chromosomes are carried to the next generation. The evolutionary property of GA is utilised in this paper to evolve to suit the changing demands. The performance and the evolutionary property of the proposed solution are shown with the simulation tests.

Conference and social events provides an opportunity for people to interact and develop formal contacts with various groups of individuals. In this paper, we propose an efficient interaction mechanism in a pervasive computing environment that provide recommendation to users of suitable locations within a conference or expo hall to meet and interact with individuals of similar interests. The proposed solution is based on evaluation of context information to deduce each user's interests as well as bioinspired self-organisation mechanism to direct users towards appropriate locations. Simulation results have also been provided to validate our proposed solution.

This paper describes a collaborative approach to handling dynamic attack threats in mobile ad hoc networks. Our approach is biologically and socially motivated. Each network node maintains a trust score for each other node of which it is aware and distributes these to its neighbourhood. Services have associated trust thresholds - the more sensitive the service, the higher the threshold. We define a robust decentralised dynamic system involving nodes, services and trust scores that helps to quickly and reliably locate potential sources of attacks and their threat level. The paper presents results of simulations of the behaviour of the system's dynamics and its interpretation in the context of ad hoc networks.

In this paper we describe an activity recognition system capable of monitoring user activities in home environments. Activities are monitored by processing information from various sensors embedded in the environment that provide information pertinent to user's actions. We utilise the concept of self-organising object networks to gather and hierarchically process information related to user actions in a distributed manner. This information is then fed to the decision module which matches this information in the user's activity map in order to deduce user's activity. The decision module comprises a Bayesian network coupled with a rule-based engine which is used to provide accurate activity inference process.

Abstract — The increase in complexity of network management systems and a consequent lack of association to business requirements has driven the need for autonomic communications. By integrating context information, autonomic computing can provide more efficient means to counter technical problems found in complex network systems and at the same time address associated business requirements. In this paper, we propose an autonomic communications architecture that manages complexity through policy-based management where we incorporate a shared information model integrated with knowledge-based reasoning mechanisms to provide selfgovernance behavior.