The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification, that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this article proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an artificial neural network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme’s robustness.

Edge intelligence is rapidly emerging as a pivotal platform for supporting future IoT networks. The integration of artificial intelligence and machine learning (AI/ML) with edge computing furnishes a new era in which edge systems can learn environment dynamics and optimize resource autoscaling policies. However, the heterogeneity of IoT networks, characterized by diverse applications and requirements, necessitates edge systems with advanced intelligence to tailor resource autoscaling policies to specific environments. Despite ongoing research in edge intelligence, most studies have focused on a single environment or service type, thereby limiting their applicability to real-world scenarios with varied IoT services. To address this limitation, we propose the intelligent and autonomous edge slicing (IAES) system, a novel approach designed to recognize diverse IoT environments and implement per-slice resource allocation policies. IAES leverages deep reinforcement learning (DRL), namely, dueling double deep Q-networks (D3QN), to optimize resource autoscaling across distinct IoT environments, such as smart cities, eHealth, and smart factories. Additionally, IAES incorporates an intelligent environment classification component that utilizes joint traffic prediction and classification models. Several AI algorithms such as long short-term memory (LSTM), convolutional neural networks (CNN), and multilayer perceptron networks (MLP), are evaluated for their efficacy in predicting IoT environments. Simulation experiments demonstrate that the IAES system achieves a 50-60% reduction in system costs compared to both rule-based commercial autoscaling employed in Kubernetes systems and an intelligent prediction-based autoscaling algorithm.

The proliferation of Internet of Things (IoT) services across diverse sectors such as healthcare, industrial IoT, and smart cities has introduced unprecedented complexity in network Management and Orchestration (MO). Contemporary MO systems are challenged to support the coexistence of IoT services with varying Quality of Service (QoS) requirements while ensuring end-to-end (E2E) performance across heterogeneous technologies and multi-administrator domain networks. To address these requirements, emerging technologies such as Intelligent Intent-Based Network Slicing (I-IBNS) systems are increasingly employed, leveraging advancements in network automation, Artificial Intelligence (AI), and Network Slicing (NS). This paper focuses on three critical challenges in developing I-IBNS systems for IoT: E2E resource allocation, privacy preservation in multi-administrator systems, and E2E QoS assurance across heterogeneous networks and time-varying traffic. We propose a privacy-preserving I-IBNS framework, named Harmony Slice Master (H-SliceMaster), which integrates a knowledge management framework for privacy-aware data aggregation, an intent propagation mechanism for translating high-level intents into network configurations, and a novel Promise and Price Network Operation (PPNO) principle for optimizing E2E resource allocation while maintaining intra-domain privacy. A proof-of-concept design of the H-SliceMaster is presented, utilizing Deep Q-Networks (DQN) for intra-domain resource allocation and a centralized algorithm for optimizing E2E network slice deployment. Simulation results demonstrate that the H-SliceMaster efficiently satisfies various IoT applications and delay requirements. Moreover, the proposed system achieved an 20% and 50% reduction in system costs compared to a Branching Dueling Q-Network (BDQ) and greedy algorithm, respectively.

The rapid integration of intelligence and automation into future Internet of Things (IoT) systems, empowered by Intent-based Networking (IBN) and Network Slicing (NS) technologies, is transforming the way novel services are envisioned and delivered. The automation capabilities of IBN depend significantly on key facilitators, including data management and resource management. A robust data management methodology is essential for leveraging large-scale data, encompassing service-specific and network-specific data, enabling IBN systems to extract insights and facilitate real-time decision-making. Another critical enabler involves deploying intent-based mechanisms within an NS system that translate and ensure user intents by mapping them to precise Management and Orchestration (MO) commands. Nevertheless, data management in IoT systems faces significant security and operational challenges due to the diverse range of services and technologies involved. Furthermore, intent-based resource management demands intelligent proactive, and adaptive MO mechanisms that can fulfill a wide range of intent requirements. Existing surveys within the field have focused on technology-specific advancements, often overlooking these challenges. In response, this paper defines Intelligent Intent-Based Network Slicing (I-IBNS) systems exemplifying the integration of intelligent IBN and NS for the MO of IoT systems. Furthermore, the paper surveys I-IBNS systems, focusing on two critical domains: resource management and data management. The resource management segment examines recent developments in IBN mechanisms within an NS system. Meanwhile, the second segment explores data management complexities within IoT networks. Moreover, the paper envisions the roles of intent, NS, and the IoT ecosystem, thereby laying the foundation for future research directions.

Multi-Access Edge Computing (MEC) is a popular and promising paradigm that allows service providers to serve their users from nearby servers. In order to fully leverage the advantages of MEC, the mapping between users and edge servers is of utmost importance for service providers. The Edge User Allocation (EUA) problem has been widely studied from the perspective of service providers with different objectives, e.g., maximizing the number of allocated users, respecting the latency threshold, minimizing overall system cost, etc. However, service providers tend to have dynamic priorities for different objectives over time. In certain situations, a service provider may opt to prioritize the minimization of their system cost at the expense of not meeting all their users expectations, or vice-versa, throughout a range of priority degrees. In this paper, we present a Deep Reinforcement Learning (DRL) approach for allocating users to edge servers according to dynamic priorities. We consider the online EUA problem where users arrive and depart dynamically, and propose a distributed solution that does not require full observation of all the servers to make allocation decisions. We offer a solution for both user-satisfaction and cost-effectiveness, while allowing the service provider to adjust their priority for each objective. A series of experiments have been conducted to evaluate the performance of our approach, under different priority degrees, against other baseline approaches. The results show the potential benefits of the proposed scheme in providing an adjustable multi-option solution for service providers.

Machine learning-based intrusion detection systems (IDSs) serve as a defense-in-depth layer for Internet of Things (IoT) networks by detecting potential intrusions within IoT traffic. However, resource-constrained IoT devices impose sig-nificant challenges in developing effective IDSs. Recently, fed-erated learning (FL) has emerged as a promising solution for training detection models on distributed IoT devices without compromising resource limitations resulting in the introduction of FL-based IDSs. To this end, this paper introduces a novel approach to enhance the effectiveness of current FL-based IoT IDSs while utilizing the same resources. The proposed system partitions the FL rounds between IoT device groups, allowing each group to update the FL detection model during its time partition. Hence, by implementing this temporal partitioning, multiple detection models are updated within one FL round using the same IoT resources. The main design goals of the proposed approach are to improve detection accuracy and convergence time compared to the traditional approach. For this purpose, the proposed approach is evaluated and compared to the traditional approach using five intrusion scenarios on IoT traffic obtained from the Edge-IIoTset dataset. The results demonstrate that the proposed temporal partitioned FL-based IoT IDS outperforms the traditional system by achieving higher detection accuracy and faster convergence time. Furthermore, the proposed approach achieves the required detection accuracy in fewer FL rounds, which can, in principle, save more IoT resources.

Information-centric Internet of Things (IoT) sensor networks allow users to access data directly from the sensing layer. This is done through cluster heads (CHs), which are selected as a result of IoT nodes’ clustering. To respond to users’ data requests, CHs aggregate, encrypt, and store locally sensed data in rounds. For data encryption, security resources are allocated to sensor nodes every round. To satisfy user security needs, very often, security resources are overutilized leading to higher energy consumption and shorter network lifetime. Meanwhile, sensor nodes’ and users’ mobility may result in link failures. Therefore, efficient clustering and security resource allocation is required to ensure users’ data and security needs are satisfied while optimizing network resource utilization. Graph convolution networks (GCNs) can help to address this challenge. GCNs perform learning on graphs while considering non-Euclidean nodes’ relations and features. Using GCNs, user awareness, and IoT nodes’ features can be incorporated into the cluster-based management of mobile information-centric IoT sensor networks. Therefore, this article proposes user-aware clustering with security resource allocation (USRA) using GCNs. In USRA, the proposed clustering algorithm improves communication reliability by optimizing users’ and nodes’ coverage. Meanwhile, the proposed security resource allocation plan prevents overutilization of security resources by considering user security needs in each cluster. Compared to existing works, USRA achieves lower energy consumption on security while ensuring high user security satisfaction. This promotes a longer network lifetime. USRA further contributes to higher communication reliability and throughput with stable data delivery latency to users.

Traffic modeling is a valuable tool for simulating traffic characteristics and assessing the effectiveness of new network mechanisms and protocol designs. The emergence of the Internet of Things (IoT) has led to a growing interest in IoT traffic modeling due to the unique characteristics of IoT traffic, such as sudden data bursts and application-dependent traffic characteristics. The focus of the literature has been on modeling the arrival distribution of IoT traffic. However, this approach fails to capture important characteristics of time-series traffic, such as IoT traffic behaviors and seasonality patterns. Such characteristics provide crucial insights for the effective management and optimization of IoT networks. By exploiting time-series characteristics, dynamic resource allocation mechanisms can be designed instead of resource provisioning for peak usage. Additionally, comprehending the traffic generation behavior of IoT sensors can provide insight into the energy consumption of the sensor layer, which has a multitude of uses. In this article, we present a novel IoT traffic modeling framework called the tiered Markov-modulated stochastic process (TMMSP). The TMMSP framework can produce application-specific IoT time-series traffic traces that mimic the behaviors, e.g., the temporal dynamics, of real IoT traffic. Our results illustrate the flexibility and capability of the TMMSP framework in modeling the traffic behaviors of three IoT applications, specifically, telehealth, asset monitoring, and building security applications. Finally, we illustrate how the TMMSP framework can be used to evaluate the performance of an autonomous edge slicing (AES) mechanism.

The integration of Internet of Things (loT)-based solutions in various applications introduced several challenges in security and privacy. Due to the nature of loT systems, traditional security solutions are not suitable for solving these challenges. Researchers introduced trust management as a viable solution due to its ability to track the dynamic behavior of loT devices. Compared to traditional security solutions, trust does not require an extensive amount of resources. Several loT trust solutions rely on distributed models that increase network overhead and consume additional energy. To this end, this work proposes a trust management scheme for loT systems that can be implemented at the access layer of loT systems. The proposed scheme establishes trust for loT devices through device-system interaction and communication attributes without requiring any additional information or modifications to the device. The trust value is computed using the trust attributes over a specific window size of interactions and using a forget factor. Simulation results show the ability of the proposed scheme to track the behavior of loT devices. Results also show that the proposed scheme maintains high performance in detecting persistent attacks compared to existing schemes from the literature, while improving the detection rate of ON-OFF attacks by 15%.

Nowadays, services and applications are becoming more latency-sensitive and resource-hungry. Due to their high computational complexity, they can not always be processed locally in user equipment, and have to be offloaded to a distant powerful server. Instead of resorting to remote Cloud servers with high latency and traffic bottlenecks, service providers could map their users to Multi-Access Edge Computing (MEC) servers that can run computation-intensive tasks nearby. This mapping of users to MEC distributed servers is known as the Edge User Allocation (EUA) problem, and has been widely studied in the literature from the perspective of service providers. However, users in previous works can only be allocated to a server if they are in its coverage. In reality, it may be optimal to allocate a user to a distant server (e.g., two hops away from the user) if the latency threshold and system cost are both respected. This work presents the first attempt to tackle the multi-hop aware EUA problem. We consider the static EUA problem where users have a simultaneous-batch arrival pattern, and detail the added complexity compared to the original EUA setting. Afterwards, we propose a game theory-based distributed approach for allocating users to edge servers. We finally conduct a series of experiments to evaluate the performance of our approach against other baseline approaches. The results illustrate the potential benefits of allowing multi-hop allocations in providing better overall system cost to service providers.

Privacy concerns arise from sharing electric load data. For instance, this data can be hijacked and deductions can be made about building occupancy. Anonymized data does not fully solve the issue, as there have been several successful attempts of re-identifying individuals from anonymized data. Federated Learning (FL) involves training a global model among several clients at the edge without sharing data, but instead, sharing model weights. Besides training a global model to reduce the universal error, it is important to focus on reducing the local error for each client. Personalization helps improve local model convergence without affecting the global model learning process. Therefore, individuals can have a more accurate understanding of their energy usage behavior. It also reduces the number of FL rounds needed for training, which reduces load on the smart grid edge computing resources, communication network, and therefore reduces costs. Previously, research has only looked at stochastic gradient descent (SGD) and Adam for fine-tuning clients’ local models at the edge in the process of FL. This research simulates different FL environments to explore electric load forecasting using different FL aggregation algorithms at the server, as well as seven local optimizers for FL personalization (SGD, Adam, Adagrad, Adadelta, Adamax, Nadam, and RM-SProp). An analysis of the different local optimizer algorithms is also presented.

In heterogeneous cluster-based information-centric wireless sensor networks (ICWSNs), sensor nodes acquire different application-specific data. They are clustered based on proximity, where cluster heads (CHs) act as cache nodes. Meanwhile, grouping sensor nodes based on the information type gathered can improve the ICWSN performance. Motivated by the heterogeneous nature of ICWSNs, application-specific communities can be formed within each cluster, where community leaders (CLs) can be selected to cache application-specific data. In this case, CHs gather aggregated data from CLs rather than basic sensing nodes. However, this creates an issue of the energy–latency and security tradeoff and affects user security satisfaction. In this work, we propose solving this issue by studying cluster-based ICWSNs with heterogeneous communities and comparing them to conventional heterogeneous cluster-based ICWSNs. Based on the formulated analytical model, we then propose SLAC-H, a security-level-aware CHs’ and CLs’ selection algorithm for cluster-based ICWSNs with heterogeneous communities. SLAC-H addresses the energy–latency and security issue by optimizing energy and coverage supported by sensor nodes in a cluster-based ICWSN with heterogeneous communities subject to security constraints. Simulation results show that compared to existing works, SLAC-H achieves lower latency and energy consumption while fulfilling higher user security satisfaction.

The wide adoption of large-scale Internet of Things (IoT) systems has led to an unprecedented increase in backhaul (BH) traffic congestion, making it critical to optimize traffic management at the network edge. In IoT systems, the BH network is supported by various backhauling technologies that have different characteristics. Also, the characteristics of the BH links can be sometimes time varying and have an unknown state, due to external factors such as having the resources shared with other systems. It is the responsibility of the edge devices to be able to forward IoT traffic through the unknown-state BH network by selecting the suitable BH link for each collected data flow. To the best of our knowledge, this type of BH selection problem is not addressed in the literature. Therefore, there is a crucial need to develop intelligent approaches enabling edge devices to learn how to deal with unknown-state (partially observable) components of the BH network, which is the primary goal of this article. We propose an edge-based BH selection technique for improving traffic delivery by exploiting multiobjective feedback on delivery performance. The proposed approach relies on the advantage-actor–critic deep reinforcement learning (DRL) methods. Moreover, to improve the DRL training performance in large-scale deployments of distributed IoT systems, federated learning (FL) is applied to enable multiple edge devices to collaborate in training a shared BH selection policy. The proposed federated DRL (F-DRL) approach is able to solve the BH selection problem as verified and demonstrated through extensive simulations.

Future wireless networks will exhibit heterogeneity of traffic generating sources originated by numerous Internet of Things (IoT) nodes as well as traditional mobile phones. Moreover, the space of novel IoT services is expanding the simple monitoring tasks of IoT nodes to more complex services in which a node can be in a monitoring state and transition autonomously to an alarm state when predefined conditions are detected. The complexity of the envisioned future wireless networks is indeed new to the community with challenges affecting many aspects such as protocol design and network operation mechanisms. Traffic modeling lies at the core of these issues. As the advancement of technologies continues, faithful performance evaluation measures are dependent on the underlying traffic model. In this scope, we propose a Tiered Markov Modulated Poisson Process (TMMPP) that is capable of capturing IoT traffic characteristics, e.g. patterns and seasonality, which occur in long time spans, e.g days, with the flexibility of modeling different IoT service behaviors. Moreover, we study an autonomous edge scaling mechanism as a use case illustrating the benefits of the proposed TMMPP traffic model.

COVID-19 pandemic has brought major uncertainty in load forecasting. Enforcing and relaxing lockdown rules, infection numbers, and the changing habits of people are the main causes of this uncertainty. Electric load forecasting maintains the balance between electric supply and demand. It also assists electric utilities in pricing their services, planning, and managing their infrastructure. This paper proposes two pandemic-aware load forecasting models (i) a city-level model, applied on the cities of Ottawa and Toronto, predicting hourly load using weather and pandemic-related features including population mobility and the number of daily COVID-19 infections, and (ii) a second open-source model forecasting quarter-hourly residential-level loads using weather and population mobility features for the city of Pune in India. Both models utilize multitask learning to jointly learn and predict future electric loads. The quarter-hourly model uses Bi-directional Long Short-Term Memory (LSTM) to learn from COVID’s specific features, and a Convolutional Neural Network (CNN) to learn from the historical load data before the pandemic. The multitask nature of the model allows for incorporating multiple datasets with different numbers of features. The residential-level multitask model allowed for learning from long-term data before COVID-19 using weather features, short-term load data, and the mobility data. Multitask learning has also enabled the use of two datasets with different numbers of features due to the lack of mobility data pre-COVID.

Intelligent transportation systems, such as connected vehicles, are able to establish real-time, optimized and collision-free communication with the surrounding ecosystem. Introducing the internet of things (IoT) in connected vehicles relies on deployment of massive scale sensors, actuators, electronic control units (ECUs) and antennas with embedded software and communication technologies. Combined with the lack of designed-in security for sensors and ECUs, this creates challenges for security engineers and architects to identify, understand and analyze threats so that actions can be taken to protect the system assets. This paper proposes a novel STRIDE-based threat model for IoT sensors in connected vehicle networks aimed at addressing these challenges. Using a reference architecture of a connected vehicle, we identify system assets in connected vehicle sub-systems such as devices and peripherals that mostly involve sensors. Moreover, we provide a prioritized set of security recommendations, with consideration to the feasibility and deployment challenges, which enables practical applicability of the developed threat model to help specify security requirements to protect critical assets within the sensor network.

Although the market valuation and adoption of IoT in various sectors is in an uptrend, the actual deployment is lagging when compared to the industrial forecasted data. The main reason behind this drawback is the lifespan of IoT devices due to their limited battery capacities. A solution to the problem is to deploy energy harvesting units (e.g., solar to replenish the batteries). However, due to the time varying availability of both irradiance and temperature and their effect on the power output, it is essential to predict both variables. To this end, we propose in this paper a prediction system that does not consume a lot of energy and that can be deployed on low computational nodes. This model consists of a Seasonal Auto Regressive Integrated Moving Average (SARIMA) with a Kalman filtering (KF) component. We build this model using an actual dataset for Ottawa, Ontario, Canada. We then demonstrate its effectiveness by presenting the results for randomly selected days in the Winter Season. In this context, we show that the SARIMA-KF outperforms the SARIMA in all scenarios with an average error reduction of 59.3%.

Internet of Things (IoT) along with Virtualized Network Function (VNFs) are creating a wide variety of opportunities for emerging vertical applications. Network operators are faced with a strategic puzzle on how to balance limited resource availability, dynamic IoT traffic requirements, and dynamic IoT device behavior in an end-to-end communication paradigm. To this end, this paper formally defines the IoT-aware VNF Placement (IVP) problem. We then evaluate an indicative set of placement algorithms with different objective functions under static and dynamic traffic scenarios to study their impact on the overall performance. The algorithms are evaluated based on realistic IoT traffic statistics in a smart-city environment and are presented as a simulation-based case study. Evaluation results emphasize the critical impact of considering multi-objective algorithms to accurately capture a set of conflicting goals, while efficiently balancing between them when solving the IVP problem. Finally, we shed light on the importance of using sophisticated lightweight approximation algorithms, to alleviate the inadequacies of the optimal mathematical solution.

In cluster-based information-centric wireless sensor networks (ICWSNs), mobile sensor nodes are grouped into clusters in rounds. In each cluster, a cluster head (CH) is selected, which collects, aggregates, and forwards locally sensed data to a sink node. CHs further store a copy of data for the round period to act as cache nodes and deliver data to mobile users upon requests. Nevertheless, clustering and securing mobile ICWSNs are challenging. This is because, in addition to sensor nodes’ and users’ mobility, sensor nodes are often resource constrained. Therefore, clustering and security resource allocation in mobile ICWSNs should be carefully redesigned to ensure efficient ICWSN operation, data security, and timely data access to mobile users. This article proposes a node embedding with security resource allocation (NESRA) clustering algorithm for mobile ICWSNs in rounds. NESRA allocates security resources to sensor nodes based on the location, mobility, and energy resources available in the first step. An optimization problem is formulated to select CHs that maximize network coverage and minimize data delivery delay to mobile users in the second step. In the third step, NESRA utilizes network representation learning that embeds sensor nodes’ location, mobility, and expected energy expenditure features into a 2-D space to form well-separated clusters of sensing nodes. Compared to existing works, NESRA achieves lower energy consumption, nodes’ death rate, and latency and allows higher throughput and cache nodes’ utilization with stable data security. Still, NESRA has some challenges to overcome in high-mobility networks.

Internet of Things (IoT) systems are driven by the massive data generation at the sensing layer. Most of data management protocols in the sensing layer target optimizing different Quality-of-Service (QoS) metrics, such as data rate, packet loss, and delay, while taking the limitations of wireless sensing networks (WSNs) into consideration. However, it is also critical to consider improving the quality of collected data, especially when the WSNs are congested by delay-sensitive data packets, leading to high packet loss. While Value-of-Information (VoI), which represents data freshness and time relevance, is commonly used to define data value, it does not provide a notion of data recoverability and tolerance-to-loss. In this article, the main contribution is to develop data management schemes to cope with inevitable data loss by identifying the data portion with a higher value to the overlaying applications. We also develop a novel metric that is referred to as information content (IC), quantifying the amount of information in data. The IC is defined such that data holding information of low-probable events have higher IC than data holding information of high-probable events. In this context, the VoI and IC are exploited in developing information-oriented traffic forwarding and reduction schemes to ensure that all dropped packets are more accurately recoverable through a traffic recovery scheme, and therefore the running applications are not disrupted. Through extensive Monte-Carlo simulations, we show that the proposed information-oriented data management improves the performance in terms of data congestion, lifetime, packet loss, delay, and data recovery accuracy.

IoT applications are quickly evolving in scope and objectives while their focus is being shifted toward supporting dynamic users’ requirements. IoT users initiate applications and expect quick and reliable deployment without worrying about the underlying complexities of the required sensing and routing resources. On the other hand, IoT sensing nodes, sinks, and gateways are heterogeneous, have limited resources, and require significant cost and installation time. Sensing network-level virtualization through virtual Sensing Networks (VSNs) could play an important role in enabling the formation of virtual groups that link the needed IoT sensing and routing resources. These VSNs can be initiated on-demand with the goal to satisfy different IoT applications’ requirements. In this context, we present a joint algorithm for IoT Sensing Resource Allocation with Dynamic Resource-Based Routing (SRADRR). The SRADRR algorithm builds on the current distinguished empowerment of sensing networks using recent standards like RPL and 6LowPAN. The proposed algorithm suggests employing the RPL standard concepts to create DODAG routing trees that dynamically adapt according to the available sensing resources and the requirements of the running and arriving applications. Our results and implementation of the SRADRR reveal promising enhancements in the overall applications deployment rate.

Nodes in wireless Internet of Things (IoT) sensor networks are heterogeneous in nature. This heterogeneity can come from energy and security resources available at the node level. Besides, these resources are usually limited. Efficient cluster head (CH) selection in rounds is the key to preserving energy resources of sensor nodes. However, energy and security resources are contradictory to one another. Therefore, it is challenging to ensure CH selection with appropriate security resources without decreasing energy efficiency. Coverage and energy optimization subject to a required security level can form a solution to the aforementioned trade-off. This paper proposes a security level aware CH selection algorithm in wireless sensor networks for IoT. The proposed method considers energy and security level updates for nodes and coverage provided by associated CHs. The proposed method performs CH selection in rounds and in a centralized parallel processing way, making it applicable to the IoT scenario. The proposed algorithm is compared to existing traditional and emerging CH selection algorithms that apply security mechanisms in terms of energy and security efficiencies.

The Internet of Things (IoT) technologies are transforming traditional businesses into digital-based platforms allowing for more service innovation, performance efficiency and customer satisfaction. Novel services enable users to utilize their personal devices (eg. mobile phones or laptops) to access the IoT platform, process data, and control the IoT infrastructure. However, these services impose critical user authentication and access control requirements. In this paper, we propose a decentralized user authentication and access control system for the IoT platforms via a permissioned blockchain network. We define an authorization sensitivity factor to provide clients with specific access control privileges and we consider an ehealth system as a use case example to demonstrate our solution. The proposed system is implemented using Ethereum platform. Besides, we investigate a threat model that considers an insider Distributed Denial of Service (DDoS) attack. The proposed defense mechanism utilizes a modifier function in the smart contract and keeps a real-time record of legitimate users to restrict function calls. The results illustrate the benefits of the defense mechanism in terms of the system response time.

Diabetes Mellitus Type II (T2D) is a Chronic Disease and is the most common type of Diabetes in the world, responsible for 95% of all Diabetes patients. T2D is a very complex disease and requires a large amount of self-management from the patient in order to maintain a healthy and threat-free lifestyle. Therefore, we develop in this paper a data analytics solution to assist in the self-management of T2D patients through several methods consisting of a rule-based system, anomaly detection, and threat forecasting.

Smart Grid is a major element of the Smart City concept that enables two-way communication of energy data between electric utilities and their consumers. These communication technologies are going through sharp modernization to meet future demand growth and to achieve reliability, security, and efficiency of the electric grid. In this paper, we implement an IPv6 based two-way communication system between the transformer agent (TA), installed at local electric transformer and various customer agents (CAs), connected to customer's smart meter. Various homes share their energy usage with the TA which in turn sends the utility's recommendations to the CAs. Raspberry Pi is used as hardware for all the CAs and the TA. We implement a self-healing mesh network between all nodes using OpenLab IEEE 802.15.4 chips and Routing Protocol for Low-Power and Lossy Networks (RPL), and the data is secured by RSA/AES keys. Several tests have been conducted in real environments, inside and outside of Carleton University, to test the performance of this communication network in various obstacle settings. In this paper, we highlight the details behind the implementation of this IPv6-based smart grid communication system, the related challenges, and the proposed solutions.

Emerging technologies such as the Internet of Things (IoT) and their anticipated massive deployment stimulate the need for developing adaptive energy efficient modulation schemes to maximize network lifetime. IoT systems are typically comprised of limited energy heterogeneous devices in the sensing layer, imposing significant challenges in developing cross-layer schemes to solve the network lifetime problem. In this paper, we present a multi-objective adaptive modulation scheme for the physical layer of a heterogeneous IoT environment. We consider channel conditions to opportunistically maximize device prioritization, energy efficiency, and spectral efficiency. The problem is modeled as a Mixed Integer Linear Program (MILP) in GAMS and is solved by CPLEX under Rayleigh fading channel conditions. Performance evaluations show that considering device heterogeneity is crucial in order to exploit energy savings and spectral efficiency in IoT sensing nodes.

The next generation of Mission Critical Networks (MCNs) will likely be based on Long Term Evolution (LTE) technology. This is due to the many features LTE can offer such as reliable broadband communications and Proximity Services (ProSe). However, the environments where MCNs mostly work require irregular deployment of LTE. Such requirements can impose constraints on the Frequency Reuse (FR) algorithms and Medium Access Control (MAC) schedulers to be used. In this paper, we develop a high-level parameter selection framework to enable the testing of irregular deployments of LTE-based MCNs. The developed tool provides a Graphical User Interface (GUI) that can be easily used to change the system settings and the available resources in order to find the most suitable system parameters. The developed algorithm also considers minimizing the used spectrum and allows the end-user to select a Quality-of-Service (QoS) threshold to control the system performance. Results show that the proposed framework provides accurate recommendations on the needed resources, FR algorithms, and MAC schedulers to be used. Insights on the system performance and suggested deployment style are also provided.

Spatial and temporal correlation among the generated traffic in wireless sensor networks (WSNs) can be exploited in reducing the energy consumption of continuous sensor data collection. Dual prediction (DP) and data compression (DC) schemes rely on the spatio-temporal correlation to reduce the number of transmissions across WSNs, which leads to conserving energy and bandwidth. In this paper, we present both schemes in a two-tier data reduction framework. The DP scheme is used to reduce transmissions between cluster nodes and cluster heads, while the DC scheme is used to reduce traffic between cluster heads and sink nodes. For both schemes, various algorithms will be studied and compared in terms of accuracy, delay, and transmission reduction percentage. For the DP scheme, neural networks (NNs) and long short-term memory networks (LSTMs) are proposed to perform predictions. The training phase of the NNs and LSTMs is done online which is necessary in the DP scheme. The performance will be compared to popular least-mean-square approaches. Regarding the DC scheme, principal component analysis, non-negative matrix factorization, truncated-singular value decomposition, and discrete wavelet transform will be discussed and compared. This paper focuses on comparative analysis of various data reduction algorithms alongside the proposed ones. Finally, design challenges and open research areas for having more transmission reductions will be presented.

The Internet of Things (IoT) has enabled unprecedented interactions with our physical world, with the aim to deliver a wide range of customizable services in many domains. With recent advancements in IoT technology, users are increasingly expecting these services to be intelligent and context aware. Nevertheless, there is still no framework capable of delivering personalized IoT services on a large scale. For such a framework to be conceived, it is likely that technologies from many domains have to be utilized. This paper examines the readiness of the leading state-of-the-art technologies in several key fields for realizing the goal of a truly scalable and personalized IoT experience. We discuss the important requirements and challenges for realizing this goal. Then, we identify the major approaches that can contribute to this goal and categorize them into: technologies for adaptive personalized sensing, scalable solutions for user-centric networking, and intelligence techniques that leverage context awareness and adaptability at the application and system levels. In the first category, our discussion centers around virtualization and reprogrammability at the sensing layer. In the second category, we investigate the readiness of Fog computing and information-centric networking to develop scalable personalized IoT infrastructures. These approaches were chosen for their combined ability to match dynamic user requirements with available system resources, while guaranteeing overall efficient utilization. Finally, in the third category, we examine context awareness, reasoning, and machine learning techniques as well as semantic technologies for realizing proactive and adaptive intelligent IoT systems and applications. This paper offers a focused discussion of the key topics that drive the research in the important and timely topic of scalable and personalized IoT networks.

Multi-band cooperative spectrum sensing can provide access to a wide range of spectrum in cognitive radio networks (CRNs). The design of multi-band spectrum sensing is very challenging mainly due to scheduling of secondary users (SUs) to sense a subset of channels. In this paper, we propose a distributed learning-based multi-band multi-user cooperative spectrum sensing (M2CSS) scheme to select most appropriate SUs to sense channels. The proposed scheme allows SUs to sense multiple channels, and consists of two stages: 1) leader selection for each channel, and 2) selection of corresponding cooperative SUs to sense these channels. We formulate an optimization problem to select leaders that can effectively communicate with other SUs subject to the constraint that each SU can act as a leader for only one channel, and there will be only one leader for each channel. We then formulate another optimization problem to select corresponding cooperative SUs for each channel. After this stage, selected cooperative SUs sense channels, and use consensus learning to determine the availability of channels in a distributed manner. Simulation results show that the proposed M2CSS scheme can enhance detection performance, avoid the choice of redundant cooperative SUs, owning similar sensed information, and provide fair energy consumption for all channels compared to the existing schemes.

With the popularity of smart electrical appliances and home energy management systems, there has been a massive amount of data generated by the power consumption. This data can be beneficial for the utility as it provides the behavior patterns of customers, and thus useful decisions can be made to optimize the load on the grid. In this work, we establish a bidirectional communication system between some homes through the customer agents (CAs), which are installed at home, and the transformer agent (TA) which is installed at the local transformer. Once data is collected at the TA, it is sent to the cloud through LTE. We then use IBM Cloud services to filter and analyze this data to forecast energy consumption and make recommendations to different customers based on their real-time changing behaviors. To this end, we use six different machine learning models predicting the energy consumption: support vector regression (SVR) using linear kernel, SVR using Gaussian kernel, SVR using the polynomial kernel, linear regression, polynomial regression, and feed- forward neural networks (FFNN). To measure the accuracy of these models, we compute three different error metrics, the normalized mean absolute percentage error (NMAPE), the normalized root mean square error (NRMSE), and R2also known as the coefficient of determination. Based on these results, we observe that the performance of the forecasting model depends on the dataset properties including the size and variations. For example, while linear and polynomial regressions perform well for small-scale datasets, FFNN gives higher accuracy for large-scale datasets.

In this paper, we consider an overlay cognitive radio (CR) scenario where the primary transmitter (PT) and the primary receiver (PR) communicate via the help of a secondary users' (SUs) system. Under a worst-case scenario, we assume that the link between the primary users (PUs) is broken and the help of a selected secondary transmitter (ST) is required. Taking advantage of this opportunity, this ST will be able to transmit its own data. The communications of the PUs and the SUs take place over two phases. In the first phase, receive space shift keying (R-SSK) is employed at the PT in order to activate one ST for reception. This ST is scheduled to transmit its own data during the second phase using conventional SSK, which also allows the PR to decode the PT's message. The proposed scheduling scheme is initiated by the PT based on its incoming bits which provides fairness among STs. The proposed system comes with other advantages including the low receivers' complexity and the improved energy efficiency (EE) all gained by the use of SSK. We analyze the performance of the proposed scheme in terms of the average bit error probability (ABEP). We finally provide comparisons to existing schemes and we generate numerical results through which we confirm the derived analysis and we demonstrate the effectiveness of the proposed overlay cognitive scheduling scheme.

Energy and spectral efficient solutions are indispensable to the success of Internet of Things (IoT). The design and development of energy and spectral efficient solutions for IoT are very challenging mainly because of the large-scale deployment of a massive number of sensors and devices. Energy harvesting and cognitive radios (CRs) are considered as promising technologies for energy and spectral efficiency, respectively. In this paper, we propose an energy and spectrum efficient scheme for CR sensor networks (CRSNs). We present an architecture of CRSNs for IoT, in which sensor nodes can access the spectrum opportunistically and harvest energy from ambient radio-frequency sources. We then propose an energy management scheme that consists of: (1) energy-aware mode switching strategy which allows sensor nodes to perform dedicated energy harvesting based on their current energy level and (2) cluster head selection algorithm which considers current and average of past energy levels of sensor nodes to achieve a balance between network performance and lifetime. Furthermore, for reliable intracluster reporting, we propose a channel management strategy to assign the best quality channel to the sensor nodes in terms of stability and reliability. Extensive simulation results demonstrate the effectiveness of the proposed energy and spectrum efficient scheme and show superiority over existing schemes.

The proliferation of the Internet of Things (IoT) demands a diverse and wide range of requirements in terms of latency, reliability, energy efficiency, etc. Future IoT systems must have the ability to deal with the challenging requirements of both users and applications. Cognitive fifth generation (5G) network is envisioned to play a key role in leveraging the performance of IoT systems. IoT systems in cognitive 5G network are expected to provide flexible delivery of broad services and robust operations under highly dynamic conditions. In this paper, we present multiband cooperative spectrum sensing and resource allocation framework for IoT in cognitive 5G networks. Multiband approach can significantly reduce energy consumption for spectrum sensing compared to the traditional single-band scheme. We formulate an optimization problem to determine a minimum number of channels to be sensed by each IoT node in multiband approach to minimize the energy consumption for spectrum sensing while satisfying probabilities of detection and false alarm requirements. We then propose a cross-layer reconfiguration scheme (CLRS) for dynamic resource allocation in IoT applications with different quality-of-service (QoS) requirements including data rate, latency, reliability, economic price, and environment cost. The potential game is employed for crosslayer reconfiguration, in which IoT nodes are considered as the players. The proposed CLRS efficiently allocate resources to satisfy QoS requirements through opportunistic spectrum access. Finally, extensive simulation results are presented to demonstrate the benefits offered by the proposed framework for IoT systems.

We propose in this paper a physical‐layer security (PLS) scheme for dual‐hop cooperative networks in an effort to enhance the communications secrecy. The underlying model comprises a transmitting node (Alice), a legitimate node (Bob), and an eavesdropper (Eve). It is assumed that there is no direct link between Alice and Bob, and the communication between them is done through trusted relays over two phases. In the first phase, precoding‐aided spatial modulation (PSM) is employed, owing to its low interception probability, while simultaneously transmitting a jamming signal from Bob. In the second phase, the selected relay detects and transmits the intended signal, whereas the remaining relays transmit the jamming signal received from Bob. We analyze the performance of the proposed scheme in terms of the ergodic secrecy capacity (ESC), the secrecy outage probability (SOP), and the bit error rate (BER) at Bob and Eve. We obtain closed‐form expressions for the ESC and SOP and we derive very tight upper‐bounds for the BER. We also optimize the performance with respect to the power allocation among the participating relays in the second phase. We provide examples with numerical and simulation results through which we demonstrate the effectiveness of the proposed scheme.

Due to the critical importance of Public Safety Networks (PSNs), they must have regular on-site performance evaluation. Conducting tests on real systems are expensive in terms of money, efforts, resources, and time. On the other hand, simulation and emulation tools are very important in the design and modeling of engineering systems. They can play a significant role in reducing the high expenses of such periodic tests. NS-3 is a widely used network simulator with a lot of capabilities. However, simulating realistic and large-scale PSNs reveals many limitations in the performance of simulation and urges for methods to speedup the simulations. Despite the exerted efforts in creating distributed simulators, less work that targets wireless networks is done. In this paper, we survey the different methods to enhance the simulation performance including parallel and distributed solutions in the PSN context. We also propose a framework to evaluate and test PSNs in real-time manner. This paper opens the door for future work in achieving better simulations, in terms of reliability and consumed time, for PSNs' large-scale networks.

In order to advance the Mission Critical Networks (MCNs), new technologies such as Cognitive Radio (CR) has to be adopted. However, power is an important resource in CR Networks (CRNs) especially in disastrous environments where power outages are expected and communications become very important for search and rescue teams. Power allocation among Secondary Users (SUs) needs to be optimized particularly when the available transmission power is limited. In this paper, two algorithms are proposed to optimize power allocation among SUs that were successful in accessing the spectrum using hybrid interweave/underlay access scheme. The objective is to maximize the Spectral Efficiency (SE) while respecting the power budget constraints. The scenario in which the CRN has multiple SUs that are interfering with several PUs is addressed. Hence, different SUs will have different power and interference limits depending on PUs' activity. Moreover, since the complexity of the optimization algorithms can be high, a suboptimal discrete Cap-Limited Heuristic (CLH) algorithm is proposed. The CLH algorithm considers assigning power to SUs from a discrete set of power levels. Extensive simulations are performed and the results of the proposed suboptimal algorithm show a near optimal performance with lower complexity and reduced computational-cost.

Long Term Evolution (LTE) is a strong candidate for the next generation of Mission-Critical Networks (MCNs) for public safety and military use. However, extensive investigation on different operating scenarios has to be done before adopting LTE. Emulating LTE networks is the best solution to provide the needed insights to the regulators so that they can make informed decisions. In this paper, we integrate LTE-EPC Network simulator (LENA) module with the Common Open Research Emulator (CORE) in order to enable the emulation of large-scale LTE networks. The developed tool bridges the real and the simulated worlds by allowing us to connect real devices to simulated devices and passing traffic to the simulated world from the real world and vice versa. Using this tool, mission-critical scenarios could be investigated in a cost effective way.

Allowing Secondary Users (SUs) to access the licensed spectrum without causing harmful interference to the Primary Users (PUs) is crucial in enabling the Cognitive Radio (CR) technology. In order to increase the utilization of the spectrum bands, we propose a Dynamic Spectrum Allocation (DSA) algorithm that integrates both interweave and underlay spectrum access schemes. The proposed algorithm will jointly take into account the geographical locations of the nodes, the correlated shadow fading, the interference between the primary and the secondary networks, the interference between SUs that are transmitting on the same channel, and the communications activity of the users. Moreover, a suboptimal heuristic DSA algorithm that jointly takes into consideration all of the aforementioned issues, while requiring low computational- and time-costs, is developed. Simulation results show that the proposed algorithm provides a good success rate and outperforms classical spectrum allocation algorithms.

Enabling Energy Harvesting (EH) for Cognitive Radio Networks (CRNs) is promising to extend their lifetime. Since Secondary Users (SUs) can either access the spectrum or harvest energy, EH should be integrated within the context of Dynamic Spectrum Access (DSA), in order to increase both the energy efficiency and the spectrum efficiency. This paper proposes a novel algorithm that enables SUs to harvest energy with minimal impact on their spectrum access performance. The algorithm allows SUs to participate in making decisions regarding their operating mode. Moreover, the algorithm ensures that the level of energy in the CRN cannot be lower than a specific threshold. The performance is studied for two types of access schemes: interweave and hybrid underlay/interweave. The results show that the proposed algorithm provides a good balance between accessing the spectrum and harvesting energy.

Abstract Increasing the number of terminals in a cognitive radio network is known to improve the accuracy of cooperative spectrum sensing at the cost of reducing the useful communication time. This downside can be partially mitigated using decision‐based fusion and/or sequential reporting. This paper proposes a novel selective decision‐based cooperative spectrum sensing strategy that limits the reporting time to a single reporting slot with a possibility for retransmissions using automatic repeat request. The terminal with the highest energy estimate sends its local decision to the fusion center to make a final decision. Potential decoding errors are mitigated using threshold‐based automatic repeat request. The performance of the proposed strategy is studied using rigorous mathematical analysis and intensive computer simulations. Results show observable performance enhancements compared with some benchmark strategies in terms of detection accuracy and agility. Copyright © 2015 John Wiley & Sons, Ltd.

Cooperative multiband spectrum sensing presents the next generation of cognitive radio networks, where multiple bands are sensed and accessed to improve spectrum maintenance and to enhance the network throughput. In order to concretize this concept, several uniform cooperative spectrum sensing techniques have been proposed in literature which suffers from a high complexity computation over the global sensing scheme. To overcome this limitation, this work proposes a robust and efficient spectrum sensing technique. The proposal investigates a new alternative of Single-Pixel-Camera adaptation of the cooperative multiband spectrum sensing for Cognitive Radio. Instead of using the conventional uniform band measurements, we investigate a non-uniform approach, where the time of sensing is reduced by using a hard and soft decision metric. Moreover, this approach allows to enhance the performances in terms of spectrum sensing. Simulation results are given to support our claims.

Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises toward implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front–end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band-based networks.

Presents corrections to "Multiband spectrum access: Great promises for future cognitive radio networks" (IEEE Proceedings, vol. 102, no. 3 pp. 282-306, March 2014).

Reliable and energy-efficient reading of Radio Frequency IDentification (RFID) tags is of utmost importance, especially in mobile and dense tag settings. We identify tag collisions as a main source of inefficiency in terms of wasting both medium access control (MAC) frame slots and reader's energy. We propose modulation silencing (MS), a reader-tag interaction framework to limit the effect of tag collisions. Utilizing relatively simple circuitry at the tag, MS enhances the performance of existing anti-collision protocols by allowing readers to terminate collision slots once a decoding violation is detected. With shorter collision slots, we revisit the performance metrics and introduce a new generalized time efficiency metric and an optimal frame selection formula that takes into consideration the MS effects. Through analytical solutions and extensive simulations, we show that the use of MS results in significant performance gains under various scenarios.

Smart grids (SG) represent the next step in modernizing the current electric grid. In this structure, a communications network is combined with the power grid in order to gather information that can be used to increase the efficiency of the grid, reduce power consumption, and improve the reliability of services, among other numerous advantages. SG communication networks are unique in their large scale and the limited capabilities of nodes which present several challenges in the design of efficient routing protocols. This paper provides a comprehensive survey of the main networking challenges present in the design of SG communication networks, and some of the important routing protocols proposed to address those challenges. Various technologies and architectures proposed for routing in SGs are discussed. A detailed comparison of the protocols considered in this paper is also given, and key areas that require further investigation are highlighted.

This article proposes a joint power control, rate adaptation and channel selection strategy for Cognitive Radio Networks (CRNs) operating over vacant TV bands, also known as TV Bands Devices (TVBDs). To exploit the abundance of vacant channels available at the disposal of these devices, we combine the well-studied power and rate adaptation strategies with adaptive channel selection. The combined strategy maximizes the achievable throughput while guaranteeing a desired level of performance, i.e., error and outage probabilities. It also reduces the chances of causing or being subject to harmful interference to/from other co-channel users. Furthermore, to waive the processing complexity resulting from the frequent channel switching, we propose an alternative strategy that switches the operating channel only when the operating channel fails to support the least supportable data rate. For the two strategies, we derive closed form expressions for the average data rates and transmission powers. The accuracy of the derived results as well as the performance gains achieved are verified using intensive numerical and simulations results.

Spectrum sharing is an important topic in cognitive radio ad hoc networks (CRAHNs) and cognitive radio sensor networks (CRSNs). Bio-inspired consensus-based protocols can provide light-weight and efficient solutions for CRAHNs and CRSNs but the theoretical ground should be investigated for spectrum sharing fairness. In this paper, we investigate the convergence condition when applying a consensus protocol to spectrum sharing while ensuring spectrum sharing fairness. Based on the local observation and local control scheme using spectrum-related information, an individual cognitive node can effectively perform the spectrum sharing if a certain condition is met. Then we propose a consensus-based protocol for spectrum sharing. Supported with computer simulations, we show the effectiveness of using the proposed consensus-based protocol to solve spectrum sharing problems in CRAHNs and CRSNs.

Cognitive radio ad hoc networks (CRAHNs) have recently been proposed as a way to bring cognitive radio technology to traditional ad hoc networks. An important problem is to design a medium access control (MAC) protocol that addresses the decentralized control and local observation for spectrum management. In this paper, we propose a cognitive MAC protocol with mobility support (CM-MAC) based on Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) technique, where CM-MAC protocol can respond to the CRs vicinity state to primary exclusive regions. Furthermore, this paper analyzes the throughput performance for the proposed MAC protocol with the consideration of multiple primary user activities and CR node mobility. Our analytical results show that the proposed MAC protocol has desired upper bound of spectrum utilization as well as outperforms the throughput performance of CSMA/CA MAC and statistical channel allocation (SAC) MAC protocols given a certain condition.

In RFID based inventory systems, counting and estimating the number of the surrounding tags without reading each tag individually is a challenge. In this paper we propose an estimation function that considers the variance of collision and empty slots during the estimation frame. In addition, two schemes, Variance and Modulation Silencing based Estimation (VMSE) and Modulation silencing count (MSC), are proposed to utilize the accuracy of the estimation function and modulation silencing mechanism [1] in counting and estimating the number of RFID tags. In the proposed schemes, tags participating in collision and success slots are silenced to accelerate the counting process. Requiring only minimal modification to the reader-to-tag communication procedure, the proposed schemes achieve a significant performance gain when compared to existing counting protocols in the literature.

In this paper, a novel cognitive engine for Wireless Sensor Networks (WSN) is proposed in order to achieve its end-to-end goals. This engine is designed using the tool known as Weighted Cognitive Maps (WCM). WCMs have the advantage of being able to consider multiple conflicting objectives and constraints with low complexity. Their inference properties also allow them to resolve complex network interactions using simple mathematical operations. Methods for designing the WCM system are illustrated. The performance of the proposed system is evaluated using computer simulations. Simulation results show that the WCM system outperforms its existing counterparts in metrics of network lifetime, throughput, and PLR.

This article studies the performance of Decode and Forward Incremental Relaying (DF-IR) in the presence of multiple dissimilar sources of Co-Channel Interference (CCI) at both, the relay and the destination. In particular, we consider the case where all links experience flat Rayleigh fading with Krand Kdsources of CCI affecting the relay and the destination, respectively. The performance is studied in terms of three performance metrics, the normalized average spectral efficiency, the outage probability, and the error probability. The derived results unveil the incurred losses due to CCI. The accuracy of these results were verified using intensive Monte Carlo simulations.

Federating partitioned Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM), where the deployed sensor nodes are prone to significant damage and harsh operational conditions, becomes a necessity to prolong the WSN lifetime. Consequently, redundancy-based deployment strategies have been extensively studied in the literature. However, federating WSNs using node redundancy is expensive in OEM due to large-scale targeted areas, and frequent node/link failures. A natural choice in defeating these challenges is to employ multiple Data Collectors (DCs) that provide extendable and sustainable WSNs in harsh environments for long lifetime intervals. In this paper, we propose a grid-based deployment for DCs in which they are optimally repositioning on the grid vertices to connect disjointed WSN sectors. Towards this optimality, we design an Optimized DCs Repositioning (ODR) approach that maximizes the federated WSN lifetime while maintaining cost and connectivity constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.

This article analyzes the performance of a Decode- and-Forward (DF)-based selection relaying scheme in a Cognitive Radio Relay Network (CRRN). In particular, exact closed form expressions are derived for the outage and error probabilities over dissimilar and independent and identically distributed (i.i.d.) Rayleigh fading channels. The derived expressions expose the dependence of the two performance metrics on the spectrum utilization efficiency of the the Primary User (PU) as well as the accuracy of the spectrum sensing method used. The accuracy of the derived results is verified through computer simulations.

This article proposes a novel Cooperative Spectrum Sensing (CSS) scheme for Cognitive Radio Networks (CRN). The proposed scheme, referred to as the Selective Cooperative Spectrum Sensing (SCSS) scheme, is capable of jointly reducing the reporting overhead and mitigating faulty reporters at the Base Station (BS). These goals are achieved by exclusively using reliably taken and delivered reports from the individual nodes to the BS. Two conditions are used to guarantee this, one on the detection reliability while the other is on the reporting reliability. These two conditions are designed to offer flexible performance, complexity and overhead tradeoff. The performance of SCSS is studied analytically and using simulations. Results show performance enhancements compared to conventional CSS schemes.

Nowadays Wireless Sensor Networks (WSNs) are used to provide vast coverage areas in environmental applications, and thus relay nodes with wide transmission ranges are employed. However, these relays usually operate under harsh conditions with a very limited energy resources, making the network very prone to severe node failures and disconnectivities. In this paper, we propose a proactive Optimized Relay Repositioning (ORR) approach in which relays are regularly repositioned to maintain a specific level of fault-tolerance in addition to minimize the total network energy consumption. ORR is a grid-based approach, in which nodes are placed on grid vertices to limit the huge search space in large-scale environmental applications. This approach is formulated as a Mixed Integer Linear Program (MILP) for solid mathematical solutions. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant approach can introduce a significant lifetime extension as compared to other heuristic and MILP-based approaches.

Federating Wireless Sensor Networks (WSNs) in Outdoor Environment Monitoring (OEM) becomes a necessity as advances in sensing technologies are achieved. Where several WSN sectors pursuing identical/different tasks intend to collaborate with each other in order to achieve more sophisticated and challenging missions, or intend to recover a significant damage in the network. Connecting (federating) these sectors is an intricate task due to the huge distances between the sectors, and the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes that provide vast coverage areas and sustain the network connectivity in harsh environments. However, these relays are expensive and thus, the least number of such devices has to be populated. In this paper, we propose a grid-based deployment for relay nodes in which the relays are efficiently placed on the grid vertices to connect the disjointed WSN sectors. Towards this efficiency, we design an Optimized Relay Placement (ORP) approach that maximizes the disjointed sectors connectivity while maintaining cost constraints. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments.

ABSTRACT Advances in sensing and wireless communication technologies have enabled a wide spectrum of Outdoor Environment Monitoring applications. In such applications, several wireless sensor network sectors tend to collaborate to achieve more sophisticated missions that require the existence of a communication backbone connecting (federating) different sectors. Federating these sectors is an intricate task because of the huge distances between them and because of the harsh operational conditions. A natural choice in defeating these challenges is to have multiple relay nodes (RNs) that provide vast coverage and sustain the network connectivity in harsh environments. However, these RNs are expensive; thus, the least possible number of such devices should be deployed. Furthermore, because of the harsh operational conditions in Outdoor Environment Monitoring applications, fault tolerance becomes crucial, which imposes further challenges; RNs should be deployed in such a way that tolerates failures in some links or nodes. In this paper, we propose two optimized relay placement strategies with the objective of federating disjoint wireless sensor network sectors with the maximum connectivity under a cost constraint on the total number of RNs to be deployed. The performance of the proposed approach is validated and assessed through extensive simulations and comparisons assuming practical considerations in outdoor environments. Copyright © 2011 John Wiley & Sons, Ltd.

Energy efficiency and network lifetime are key factors in characterizing wireless sensor networks due to the limited energy of nodes. In this paper we present two approaches to routing in wireless sensor networks that utilize the ideas of node cooperation and information exchange to achieve cognition across multiple network layers. In the first proposal, nodes exchange information about their statistical channel parameters to achieve awareness of the coverage area and use this information in path choice and transmit power adaptation. In the second proposal, nodes share information about energy states and utilize this information in achieving load balancing across nodes in the network. Nodes also cooperate with each other to reduce unnecessary transmissions. We evaluate our proposals through computer simulations and the results show that the energy efficiency of our proposals significantly outperform existing techniques, thus achieving the greater goal of extending network lifetime.

Accurate and efficient localization of tags are of utmost importance for numerous existing and forthcoming RFID applications. In this paper, we introduce two novel methods for three dimensional localization of the passive RFID tags. In the first approach, namely Adaptive Power Multilateration (APM), using four RFID readers, distance estimations parameters are processed based on the minimal interrogation power and multilateration. Whereas in the second approach, namely Adaptive Power with Antenna Array (APAA), a single RFID reader equipped with horizontal and vertical smart antennas alongside with the reader's adaptive power levels are used for the tags distance estimations. The APM scheme localizes the tags with comparatively finer granularity whereas the APAA scheme supports reader's mobility and facilitates highly dense tag environments. Simulation results show that our proposed schemes provide more accurate localization than other indoor localization schemes.

Grid-based deployments of Wireless Sensor Networks (WSNs) are widely used in a multiplicity of applications. However, practical factors such as communication irregularity and placement uncertainty have to be considered for more efficient deployments. In this paper, we examine connectivity properties of the 3D grid-based deployment when sensor placements are subject to random errors around their corresponding grid locations and hindrances to wireless communication channels exist. A generic approach is proposed to evaluate the average connectivity of the deployed network. This generic approach is independent of the grid-shape, random error distributions, and the environment wireless channel characteristics. The average connectivity is computed numerically and verified via extensive simulations. Based on the numerical results, quantified effects of positioning errors and grid edge length on the average connectivity are demonstrated. Furthermore, we discuss several ways of achieving efficient grid-based deployment planning for connectivity, and illustrate these approaches through numerical examples.

In this paper, we propose two schemes for sensor and relay node placement in environmental sensing applications. The first scheme aims at maximizing the network lifetime by reducing the total energy consumption. The second does so while maintaining fault-tolerance constraints. It guarantees a lower bound on the minimum required number of faulty nodes. Both schemes are based on a 3-D hierarchical architecture, in which nodes are placed on grid vertices to limit the search space. We divide the lifetime of the network into fixed-length rounds and find the placement which reserves more energy in each round to prolong the lifetime. These problems are formulated via Integer Linear Programs (ILPs). An ILP solver is used to find the optimal placement of nodes in addition to multi-hop routing from the sensors to the base-station in both schemes. Extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity, show that our fault-tolerant scheme introduces a significant lifetime extension as compared to the first one under the same harsh operational conditions.

This paper presents a methodology, a theoretical framework, and some novel ideas on performance modeling and evaluation of application-specific cognitive wireless sensor networks applied to environmental protection. Cross-layer optimizations integrating the use of adaptive sleep, adaptive modulation and energy-aware higher layer processing and protocols are assumed. Routing and application layer processing are assumed to be dependent on lower-layer protocols, requirements, and constraints. Applications relevant to this study are forest monitoring, where the probability of network failure is the main parameter to be minimized, and endangered-species monitoring, where the probability of node failure is reduced by increasing the expected node life. Results are shown comparing expected node life of a cross-layered design to that of a traditional adaptive modulation system.

Device deployment plays a key role in the performance of any large-scale wireless sensor network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, viz. coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in a large scale harsh environment. In this paper, we explore the problem of relay node (RN) placement in 3D forestry space. We formulate a generalized RN deployment optimization problem aimed at maximizing the network connectivity with constraints on RNs count. We investigate how the number of RNs can affect the connectivity of a WSN in a harsh environment. Based on quantitative analysis of such effects, the paper sets a threshold on the minimum number of required RNs.

Maximizing network connectivity while maintaining a useful period of lifetime is a challenging design objective for wireless sensor networks (WSNs). Satisfying such objective becomes an even more intricate task in harsh operational environments such as those found in forestry applications. While much work has been presented aimed at forestry applications, only a few have addressed the unique characteristics of forestry settings, such as 3-D deployment and operational requirements. In this paper, we introduce a novel deployment strategy for relay nodes in WSNs for forestry applications. The strategy optimizes network connectivity, while guarantying specific network lifetime. Key to our contribution is a revised definition for network lifetime that is more realistic and more fitting to forestry applications. The effectiveness of our strategy is validated through extensive simulation and comparisons.

Multiple-input multiple-output (MIMO) systems have gained an enormous amount of attention as one of the most promising research areas of wireless communication. However, while MIMO systems have been extensively explored over the past decade, few schemes acknowledge the nonlinearity caused by the use of high power amplifiers (HPAs) in the communication chain. When HPAs operate near their saturation points, nonlinear distortions are introduced in the transmitted signal, and the resulting MIMO channel will be nonlinear. The nonlinear distortion is further exacerbated by the fading caused by the propagation channel. The goal of this paper is to use neural network (NN) technique for modeling and identification of time-varying nonlinear MIMO channels. NN schemes are then used to design an efficient receiver for these types of nonlinear fading MIMO channels.

Abstract This paper presents a simplified mathematical approach to evaluate the performance of any given circular constellation of 16‐level quadrature amplitude modulation (16‐QAM) in terms of symbol error rate (SER). Following this approach, with the aim to work with memoryless nonlinear satellite channels, a model is derived as a generalized form for both linear and nonlinear channels in the presence of down link additive white Gaussian noise (AWGN). The analysis provides means to calculate the optimal ring ratio (RR) and phase difference (PD) for several possible candidates of 16‐QAM circular constellations. The effects of RR and PD on the SER performance are investigated in the analysis. To overcome the nonlinear distortion, data pre‐distortion is taken into account in the study. The paper gives a general procedure for data pre‐distortion implementation for all circular 16‐QAM constellations. The analytical formulation has been extended for total degradation (TD) performance measure as a function of input back‐off (IBO) of the nonlinear amplifier. A SER performance‐comparison between different constellations for 16‐QAM systems has also been presented in this paper. The analytical results are validated by simulation. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we derive the exact symbol error rate (SER) and total degradation (TD) performances of coherent M-ary QAM constellations over nonlinear fading channels with maximum ratio combining (NMC) diversity. We analyze the combined effect of nonlinear distortion introduced by the high power amplifier (HPA) and multipath fading. Our results are used to optimize system parameters, such as ring ratios of circular QAM constellations and HPA-backoffs. Comparisons among five popular 16-ary constellations are also made for various nonlinear fading channels.

MIMO systems using multiple transmit and receive antennas on both ends of a linear wireless communications link are by now well studied. Several axes of these schemes have been explored with the underlying MIMO channel assumed linear. In this work, we investigate the effect of non-linearity in the MIMO channel. New results on symbol error rate performances of several M-QAM constellations in linear and non-linear MIMO channels are presented. The results show that for any MIMO configuration, performance degradation due to non-linearity reduces as the fading gets more severe, and for a particular fading channel, the degradation increases as the MIMO dimension is increased. Optimum ring-ratios for circular QAM constellations in MIMO channels are also reported, and compared with existing results where applicable.

Central features of future 4G mobile communication systems are high-speed data transmission (up to 1 Gb/s) and interactive multimedia services. For effective delivery of these services, the network must satisfy some stringent QoS metrics, defined typically in terms of maximum delay and/or minimum throughput. Mobile satellite systems will be fully integrated with the terrestrial cellular systems to provide ubiquitous global coverage to diverse users. The challenges for future broadband satellite systems, therefore, lie in the proper deployment of state-of-the-art satellite technologies to ensure seamless integration of the satellite networks into the cellular systems and its QoS frameworks, while achieving, as far as possible, efficient use of satellite link resources. The paper presents an overview of future high-speed satellite mobile communication systems, the technologies deployed or planned for deployment, and the challenges. Focusing in particular on nonlinear downlink channel behavior, shadowing and multipath fading, various physical channel models for characterizing the mobile satellite systems are presented. The most prominent technologies used in the physical layer, such as coding and modulation schemes, multiple-access techniques, diversity combining, etc., are then discussed in the context of satellite systems. High-speed and QoS-specific technologies, such as onboard processing and switching, mobility and resource management, IP routing and cross-layer designs, employed in the satellite systems are also discussed.

This paper applies natural gradient (NG) learning neural networks (NNs) for modeling and identification of nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter H followed by a zero-memory nonlinearity g(.). The neural network model is composed of a linear adaptive filter Q and a two-layer nonlinear neural network (NN). It is shown that the NG learning method outperforms the ordinary gradient descent method in terms of convergence speed and mean squared error (MSE) performance.

This letter applies natural gradient (NG) learning neural networks for adaptive inversion of Hammerstein systems. The system model is composed of a memoryless nonlinearity g(.) followed by a linear filter H. The inverse system is modeled by a neural network composed of an adaptive filter Q followed by a memoryless nonlinear perceptron. The adaptive filter Q aims at inverting the linear part of the system (adaptive deconvolution). The perceptron aims at inverting the memoryless function (adaptive function inversion). The adaptive system is trained using the NG descent algorithm. The letter shows through computer simulations that the NG approach outperforms the classical backpropagation algorithm in terms of mean-squared-error performance and convergence speed.

This paper studies the convergence properties of a self-organizing map (SOM) equalizer. The transmitted signal is assumed to be a 4-QAM signal corrupted by an additive white Gaussian noise (AWGN). Several theoretical results such as map ordering and stability conditions are given. These results show a good fit with computer simulations.

Digital satellite communication channels are equipped with nonlinear amplifiers (such as travelling wave tube amplifiers (TWT)) which exhibit severe performance degradation when they work near saturation. The paper proposes an adaptive neural network (NN) predistortion technique to process the transmitted data. The technique is based on two steps. In the first step, the TWT amplitude and phase conversions are modeled. In the second step, an approximation of the TWT inverse transfer function is used to implement the predistorter. The approach is applied to 16-QAM transmissions and is shown through computer simulations to out-perform classical Volterra series techniques.

The presence of nonlinear devices in several communication channels, such as satellite channels, causes distortions of the transmitted signal. These distortions are more severe for non-constant envelope modulations such as 16-QAM. Over the last years neural networks (NN) have emerged as competitive tools for linear and nonlinear channel equalization. However, their main drawback is often slow convergence speed which results in poor tracking capabilities. The present paper combines simple NN structures with conventional equalizers. The NN techniques are shown to efficiently approximate the optimal decision boundaries which results in good symbol error rate (SER) performance. The paper gives simulation examples (in the context of satellite mobile channels) and compares neural network approaches to classical equalization techniques.

The paper proposes a neural network approach to equalize time varying nonlinear channels. The approach is applied to a satellite UMTS channel composed of time invariant linear filters, a non-linear memoryless amplifier and a time varying multipath propagation channel. The neural network equalizer has a radial basis function structure. The usual k-mean clustering algorithm is replaced by a Kohonen (1995) learning rule. This results in an RBF-SOM equalizer which outperforms the LMS equalizer, and which has better recovering abilities (after passing through a high fading area) than the former RBF equalizer.

The paper proposes a radial basis function (RBF) neural network (NN) approach to satellite universal mobile telecommunication system (S-UMTS) channel equalization. Two main problems arise in S-UMTS communications: (i) non-linear distortions which are caused by the onboard nonlinear power amplifiers, and (ii) multipath propagation. This paper shows that RBF networks are particularly well suited to overcome these problems. The paper presents several simulation results which show that RBF networks outperform classical equalization techniques for different configurations of the mobile channel (mobile speed, noise level, number of paths, etc.).

In many neural network applications to signal processing, the back propagation (BP) algorithm is used for the training process. Recently, several authors have analyzed the behavior of the BP algorithm and studied its properties. The influence of the number of layers on the performance and convergence behavior of the BP algorithm remains, however, not well known. The paper tries to investigate this problem by studying a simplified multilayer neural network used for adaptive filtering. The analysis is based upon the derivation of recursions for the mean weight update which can be used to predict the weights and mean squared error over time. The paper shows also the effects of the algorithm step size and the initial weight values upon the algorithm behavior. Computer simulations display good agreement between the actual behavior and the predictions of the theoretical model. The properties of the BP algorithm are illustrated through several simulation examples and compared to the classical LMS algorithm.

The paper proposes a neural network technique to adaptively model and characterize digital satellite channels. The neural network model allows to identify each component of the channel by the use of the channel input-output signals as learning data. This technique was applied to fault detection in digital satellite links, especially those arising in on-board devices. The paper gives simulation examples of changes in the on-board filter characteristics. Our adaptive method allows to determine the origins of the changes and gives the new characteristics of the channel.

Many signal processing problems need to be solved in an adaptive way under some constraints. The paper introduces a constrained complex-valued neural network (CCNN) model. It is composed of two sub networks: a master which gives the main energy function (the error power between the master's output and a desired output), and a slave which gives a secondary energy function (related to the constraints imposed by the problem). The sum of these energy functions gives the cost function to be minimized by the CCNN. An extension of the classical back propagation algorithm to the complex plane, under some inequality constraints, is used for the training process. This model finds a natural application in the time-frequency analysis as it gives direct access to the time-frequency signature.>