Throughput Capacity Analysis of a Random Multi-user Multi-channel Network Modeled as an Occupancy Problem

In this paper, we consider a multiuser communication network where all the nodes in the network are equipped with multiple antennas and different users are identified through their spatial signatures. We present some results on the capacity of such networks. We consider two cases of interest: (i) when the transmitter (the base) has full knowledge of the channel state information (CSI) between itself and the receiving nodes (users); and (ii) when the base does not have CSI, but it does know which users currently have the best channel (e.g., measured in terms of a condition number). In the first case, we establish coordinated beamforming between the base and a number of users to whom data is to be transmitted. In the second case, partial knowledge of the quality of the users' channels will be used to transmit data to the users with better channel condition, without performing any beamforming at the base. In both cases, the base transmits independent data streams to multiple users. This guarantees a higher minimum quality of service for all the users compared to the case where the user with the best channel is selected and all the available capacity is devoted to it. In addition, we show that this approach may increase the overall capacity/throughput of the network.

The use of multi-channel MAC protocols for mobile ad hoc networks has been seen as a very potent method of improving the throughput capacity of such networks. In recent past, a number of multi-channel MAC protocols have been proposed for mobile ad hoc networks that have increased the overall network throughput by quite some margin compared to their single channel counterparts. However, there are still many issues with these protocols that need to be resolved. Clock synchronization is one of the major overhead in a majority of the proposed multi-channel MAC protocols. In this paper we propose a new asynchronous multi-channel MAC protocol that does not require clock synchronization between mobile nodes. Our proposed protocol is an asynchronous solution that uses multiple channels for data transmission. In our proposed protocol, nodes use a single base channel when the network load is considerably lower and migrates to other channels when the network load increases beyond a threshold value. Also, nodes share multi-channel cooperation messages with their neighbors to further improve throughput. We have conducted extensive simulations to compare the performance of our protocol against the traditional IEEE 802.11b single channel and MMAC protocol. Simulation results show that our proposed protocol outperforms both IEEE 802.11b and MMAC protocol.

Bernoulli and first-order Markov processes are used to approximate the output process of a class of slotted multiuser random-access communication networks. The parameters of the approximating processes are analytically calculated for a network operating under a specific random-access algorithm. The mean time that a packet spends in the central node of the star topology is calculated under the proposed approximations of the output processes of the interconnected networks. The results are compared with simulation results of the actual system. It turns out that the memoryless approximation gives satisfactory results up to a certain per-network-traffic load. Beyond that point, the first-order Markov processes performs better. >

Wireless information-centric networks consider storage one of the network primitives, and propose to cache data within the network in order to improve latency to access content and reduce bandwidth consumption. We study the throughput capacity of an information-centric network when the data cached in each node has a limited lifetime. The results show that with some fixed request and cache expiration rates, the network can have the maximum throughput order of 1/√n and 1/log n in cases of grid and random networks, respectively. Comparing these values with the corresponding throughput with no cache capability (1/n and 1/√(n log n) respectively), we can actually quantify the asymptotic advantage of caching. Moreover, since the request rates will decrease as a result of increasing download delays, increasing the content lifetimes according to the network growth may result in higher throughput capacities.

The Internet of Things (IoT) provides a way of communication between devices in everyday life. The range of IoT applications, including intelligent homes, intelligent cities, wearables, e-health, etc., is very wide. The IoT, which has diverse application areas, has been one of the most important developments in recent years. The range of applications contributes to an abundance of private information dissemination by users, which is a major safety problem. Trust Management is an essential security feature that ensures the system's reliability by monitoring device activity over time to detect its malicious activities. However, conventional IoT data management is vulnerable and weak. If the collected data is rejected or the stored data is intentionally manipulated by the internal users or targeted by an external hacker, then it is difficult for the manipulated data to be used. The IoT big data and its applications become more and better. Node authentication is a vital element in the IoT environment because it enables the users to connect securely with the system. The incorporation of IoT node authentication technologies provides safe retrieval of information and robust control of access. A detailed review of trust management strategies and their advantages and drawbacks are discussed in this paper. A study about the existing trust models in order that researchers understand how various systems work together to bring desired functionalities are discussed in this paper. A brief description of the most important trust management technologies discussed in this manuscript.KeywordsTrust managementNode validationData securityAttackersData modificationsMalicious actions

The Internet of Things (IoT) is emerging in the telecommunication sector, and will bring a very large number of devices that connect to the Internet in the near future. The expected growth in such IoT nodes necessitates appropriate traffic models in order to evaluate their impact on different aspects of networking, e.g., on signaling load in the networks, or on processing load of the data in a cloud. In this paper we analyze the characteristics of aggregated periodic IoT data based on related work, and compare them with a Poisson process as approximation for the superposed traffic as assumed in standardization. Such an approximation is crucial in order to investigate the scalability of an IoT network, as it may be impossible in practice to measure or to simulate large-scale IoT deployments. The accuracy and applicability of the Poisson process is investigated for the use case “IoT cloud”. The results show that the Poisson process may induce large errors depending on the performance metric of interest. This error must be considered by standardization and requires more sophisticated traffic models. As key contributions, we provide realistic traffic models for periodic IoT data, introduce performance metrics for quantifying the bias, and derive reference values as to when the Poisson process can be assumed for aggregated periodic IoT data.

We quantify the throughput capacity of wireless multi-hop networks with several medium access schemes. We analyze pure ALOHA scheme where simultaneous transmitters are dispatched according to a uniform Poisson distribution and exclusion schemes where simultaneous transmitters are dispatched according to an exclusion rule such as node coloring and carrier sense based schemes. We consider both no-fading and standard Rayleigh fading channel models. Our results show that, under no-fading, slotted ALOHA can achieve at least one-third (or half under Rayleigh fading) of the throughput capacity of node coloring scheme whereas carrier sense based scheme can achieve almost the same throughput capacity as node coloring.

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.

This paper focuses on the capacity of wireless ad hoc networks and analyzes the effect of key factors viz. network size, traffic patterns and detailed local radio interactions on the capacity of such networks. The capacity is evaluated with several different network layouts and traffic patterns through simulations. To demonstrate the impact of these factors, the capacity evaluation starts with a simple case of a chain of evenly spaced nodes in a network environment and progresses to a network with random traffic and randomly spaced nodes. Initially, capacity of static nodes is evaluated for various network layouts and traffic patterns. Since, in most scenarios, nodes do not travel significant distances during packet transmissions. As an enhancement, mobility of nodes is introduced into the network scenario and the performance is again evaluated. The simulations are carried out using OPNET modeler and the results obtained are presented in this report. The results are analyzed to understand the impact of these factors on the capacity and consequently suggest measures to increase the same. This work shows that the achievable capacity of ad hoc network depends on network size, traffic pattern and mobility. In a single cell topology, it is found that there is a 50% reduction in network throughput, if the node size increases from 2 to 10 nodes, whereas there is a 74% reduction in the throughput for chain topology for the same increase in node size. In a lattice topology with horizontal traffic, there is a 46 % reduction in network throughput when the lattice size increases from 4X4 to 5x5.The same percentage of reduction is observed when both horizontal and network traffic is introduced. In a random network topology with random traffic, there is an 80 % reduction in network throughput when the node size increases from 150 to 750 nodes. However, for the same scenario with the introduction of mobility to the nodes, a slight improvement is achieved with an overall 75% reduction in network throughput.

E-health’s ultimate vision is to empower stakeholders, such as patients, families and societies to engage in the decision-making and management of their own health status. The proliferation of the Internet of Things (IoT) is expected to change the way that health services are designed, implemented and delivered. However, little research has been done to investigate the IoT potential in the health sector. The IoT represents a significant technological field with enormous implications for both patients and health organizations. This paper combines process modelling and the Analytic Hierarchy Process (AHP) multicriteria analysis method in order to investigate and assess the potential of the IoT in health services quality. First, this paper, utilizes process modelling in order to capture the workflow of delivering health services to patients. It suggests that workflow modelling provides the conceptual foundation for the systematic consideration of the opportunities stemming from the IoT deployment. Further, this paper utilizes the AHP, in order to measure the potential of the IoT opportunities that can be identified along the workflow, in terms of its contribution to health services quality. An AHP questionnaire was structured and data were collected and analyzed from a group of health care experts. The results advocate the wide use of IoT in health services and argue for the applicability of the suggested approach as a means for medical organizations to design and deliver quality health services.

This work studies the power optimal dynamic scheduling problem in multi-channel multi-user wireless access networks. Users have quality-of-service (QoS) requirements on the minimum rates with statistical delay guarantees. Only one user is allowed to transmit over a channel in a given time slot. This work considers two scenarios: homogeneous and heterogeneous users. For the former scenario, the optimal scheduling policy can be derived, and an online scheduling algorithm for the optimal policy is proposed using online time-averaging without requiring a-priori known fading statistics. For the latter scenario, the optimal scheduling problem is combinatorially hard; hence, even when the fading statistics are available, computing the optimal policy is intractable. Consequently, this work develops a sub-optimal online scheduling algorithm with linear complexity which does not require a-priori known fading statistics. Moreover, the scheduling algorithm satisfies the QoS constraints for the users. Illustrative results demonstrate the performance of the proposed scheduling algorithms in various settings.

We study the throughput capacity of wireless networks which employ (asynchronous) random-access scheduling as opposed to deterministic scheduling. The central question we answer is: how should we set the channel-access probability for each link in the network so that the network operates close to its optimal throughput capacity? We design simple and distributed channel-access strategies for random-access networks which are provably competitive with respect to the optimal scheduling strategy, which is deterministic, centralized, and computationally infeasible. We show that the competitiveness of our strategies are nearly the best achievable via random-access scheduling, thus establishing fundamental limits on the performance of random- access. A notable outcome of our work is that random access compares well with deterministic scheduling when link transmission durations differ by small factors, and much worse otherwise. The distinguishing aspects of our work include modeling and rigorous analysis of asynchronous communication, asymmetry in link transmission durations, and hidden terminals under arbitrary link-conflict based wireless interference models.

Consider a time slotted multiuser random access network where M users have access to a single channel and each user attempts to send packets in a particular time slot with a certain probability, according to a predetermined access scheme. This forms an interacting system of queues and in this study, the authors consider generic access schemes that satisfy a separability condition and obtain bounds for the stability region, directly using a Lyapunov-type analysis. They apply their results to the case of random access networks with partial conflicts where any particular user has collision conflicts with only a subset of the other users. Using conflict graphs to quantify the degree of such conflicts, they obtain bounds for the stability region for both deterministic and random conflict graphs that model, respectively, static and mobile networks.

The Blockchain Random Neural Network for cybersecure IoT and 5G infrastructure in Smart Cities

Internet of Things (IoT) as a ubiquitous paradigm is a new concept in Information and Communications Technology (ICT) and has the ability to connect wireless and mobile embedded devices and things to the Internet. IoT is emerging as a key component of the Internet and a vital infrastructure for millions of smart and interconnected objects that are potentially vulnerable to different attacks. Thus, the security of resource-constrained devices in IoT is highly important. As an important solution, cryptographic algorithms are used to provide confidentiality and integrity of the transmitted data between the sender and receiver. Hence, this paper proposes a new hybrid cryptographic algorithm based on Rivest cipher (RC4), Elliptic-Curve Cryptography (ECC), and Secure Hash Algorithm (SHA-256) to protect sensitive information in IoT-based smart irrigation systems. In this paper, the RC4 key is encrypted by the ECC algorithm, and the output of this encryption process is transformed to SHA-256 for hashing and generating enigmatic data. SHA-256 algorithm encrypts RC4 based cipher text to improve data integrity. Comprehensive analysis and simulation results indicate that the proposed scheme is secure to various known attacks such as the Man-in-the-middle (MiM) attack, and has a better performance than other cryptographic algorithms. Also, the obtained results confirm the effectiveness of the proposed model and robustness in order to confidentiality based on analyzing secrecy.