DORS: A data overhead reduction scheme for hybrid networks in smart cities

Today, smart cities represent an effective digital platform for facilitating our lives by shifting all stakeholders toward more sustainable behavior. Consequently, the field of smart cities has become an increasingly important research area. The smart city comprises a huge number of hybrid networks, with each network containing an enormous number of nodes that transmit massive amounts of data, thus giving rise to many network problems, such as delay and loss of connectivity. Decreasing the amount of such transmitted data is a great challenge. This paper presents a data overhead reduction scheme (DORS) for heterogeneous networks in smart city environments that comprise five different methods: median, nonlinear least squares, compression, data merging, and prioritization. Each method is applied according to the current status of quality of service. To measure the performance of the proposed model, a simulation environment is constructed for a smart city using network simulation package, NS2. The obtained results indicate that DORS has the capability to decrease the size of transmitted data in the simulated smart city environment while attaining a notable performance enhancement in terms of data reduction rate, end‐to‐end delay, packet loss ratio, throughput, and energy consumption ratio.     

A metaheuristic‐based method for replica selection in the Internet of Things

With the advent of the Internet of Things (IoT), the count of gadgets connected to the Internet has been increased. IoT, as a modern paradigm, has been used to describe the future in which physical things like RFID tags, sensors, actuators, and cellphones can intermingle for achieving shared purposes. Also, we can employ cloud computing for storing the things' information in the IoT. However, this information has been replicated through the network for increasing availability. In this paper, due to the NP‐hard nature of the replica selection problem, an improved version of ant colony optimization (IACO) has been applied. The impact of pheromone on the chosen path is converted by ants to invert the underlying logic of ACO. Due to the existence of different IoT centers, the IACO has been employed for selecting the replicated data in the IoT where the load balancing among IoT centers has been considered. In this method, an ant chooses the ideal point for its movement; then others may not pass the track that the preceding ants have been passed. The obtained outcomes have shown that the method has outperformed the ACO, HQFR, and RTRM approaches regarding the waiting time and load balancing.     

ElectroBlocks: A blockchain‐based energy trading scheme for smart grid systems

Smart grid systems are widely used across the world for providing demand response management between users and service providers. In most of the energy distributions scenarios, the traditional grid systems use the centralized architecture, which results in large transmission losses and high overheads during power generation. Moreover, owing to the presence of intruders or attackers, there may be a mismatch between demand and supply between utility centers (suppliers) and end users. Thus, there is a need for an automated energy exchange to provide secure and reliable energy trading between users and suppliers. We found, from the existing literature, that blockchain can be an effective solution to handle the aforementioned issues. Motivated by these facts, we propose a blockchain‐based smart energy trading scheme, ElectroBlocks, which provides efficient mechanisms for secure energy exchanges between users and service providers. In ElectroBlocks, nodes in the network validate the transaction using two algorithms that are cost aware and store aware. The cost‐aware algorithm locates the nearest node that can supply the energy, whereas the store‐aware algorithm ensures that the energy requests go to the node with the lowest storage space. We evaluated the performance of the ElectroBlocks using performance metrics such as mining delay, network exchanges, and storage energy. The simulation results obtained demonstrate that ElectroBlocks maintains a secure trade‐off between users and service providers when using the proposed cost‐aware and store‐aware algorithms.     

基于OneNet云平台的智慧平安校园远程监控系统

多数学校已具备智慧平安校园的软硬件条件,但各自为政、信息分散,难以统一管理。基于OneNet云平台的物联网解决方案,构建了集云端数据通信、远距离监控、灯光控制、安防、智慧学习功能于一体的智慧平安校园监控系统。采用OneNet云端接入技术实现数据存储和处理,采用状态迁移算法有效提高了指令执行的成功率和响应度。测试结果表明,用户可通过移动APP对校园安全情况进行高效、实时、准确、可靠的远程监控。该设计方案可成功应用于智慧平安校园管理等多个领域。     

A novel energy‐aware routing mechanism for SDN‐enabled WSAN

Energy consumption is one of the most important design constraints when building a wireless sensor and actuator network since each device in the network has a limited battery capacity, and prolonging the lifetime of the network depends on saving energy. Overcoming this challenge requires a smart and reconfigurable network energy management strategy. The Software‐Defined Networking (SDN) paradigm aims at building a flexible and dynamic network structure, especially in wireless sensor networks. In this study, we propose an SDN‐enabled wireless sensor and actuator network architecture that has a new routing discovery mechanism. To build a flexible and energy‐efficient network structure, a new routing decision approach that uses a fuzzy‐based Dijkstra's algorithm is developed in the study. The proposed architecture can change the existing path during data transmission, which is the key property of our model and is achieved through the adoption of the SDN approach. All the components and algorithms of the proposed system are modeled and simulated using the Riverbed Modeler software for more realistic performance evaluation. The results indicate that the proposed SDN‐enabled structure with fuzzy‐based Dijkstra's algorithm outperforms the one using the regular Dijkstra's and the ZigBee‐based counterpart, in terms of the energy consumption ratio, and the proposed architecture can provide an effective cluster routing while prolonging the network lifetime.     

Energy consumption optimization for self‐powered IoT networks with non‐orthogonal multiple access

Energy harvesting (EH) has been considered as one of the promising technologies to power Internet of Things (IoT) devices in self‐powered IoT networks. By adopting a typical harvest‐then‐transmit mode, IoT devices with the EH technology first harvest energy by using wireless power transfer (WPT) and then carry out wireless information transmission (WIT), which leads to the coordination between WPT and WIT. In this paper, we consider optimizing energy consumption of periodical data collection in a self‐powered IoT network with non‐orthogonal multiple access (NOMA). Particularly, we take into account time allocation for the WPT and WIT stages, node deployment, and constraints for data transmission. Moreover, to thoroughly explore the impact of different multiple access methods, we theoretically analyse and compare the performance achieved by employing NOMA, frequency division multiple access (FDMA), and time division multiple access (TDMA) in the considered IoT network. To validate the performance of the proposed method, we conduct extensive simulations and show that the NOMA outperforms the FDMA and TDMA in terms of energy consumption and transmission power.     

Network‐layer security for the Internet of Things using TinyOS and BLIP

The design of standard communications and security mechanisms for resource‐constrained sensing applications and devices may provide an important contribution for its integration with the Internet and consequently towards the realization of what we nowadays identify as the Internet of Things. This vision will only be realizable if appropriate security mechanisms are available, and in this context we target the design and experimental evaluation of security mechanisms for communications at the network‐layer with sensing devices (smart objects) using the standard IPv6 protocol. Our work proposes and evaluates the usage of new compressed security headers for the network layer with smart objects. We implement and evaluate what is, as far as we know, the first proposal of security at the network layer experimentally evaluated using the TinyOS operating system and its networking stack. As we verify in the course of our evaluation study, various scenarios employing network‐layer secure communications involving smart objects are feasible, particularly when security mechanisms are designed to benefit from cross‐layer interactions that allow the optimization of expensive cryptographic operations. Copyright © 2012 John Wiley & Sons, Ltd.     

A novel compressed sensing ultra‐wideband channel estimation method based on non‐convex optimization

Due to the low power spectral density and complicated transfer propagation of ultra‐wideband (UWB) signal, it is important to estimate UWB channel accurately. But it is difficult to sample UWB signals directly due to their wider band width. However, compressed sensing (CS) theory provides a feasible way through lower sampling speed. Common CS‐UWB channel estimation methods adopt convex optimization, non‐sparse or non‐restricted form. In order to strengthen the restriction on sparsity of the reconstructed channel vector, a non‐convex optimization method is proposed in this paper to estimate UWB channel. Proposed method sets the objective function as a non‐convex optimization model using l_p–norm. This model is combined as a convex function to approximate the objective function and reconstruct the UWB channel vector iteratively. Because l_p–norm is closer to l₀–norm than l₁ and l₂–norm, its restriction on sparsity of objective vector is stricter. The simulation results show that this method can enhance reconstruction performance compared with existing CS‐UWB channel estimation methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

Structural and behavioral reference model for IoT‐based elderly health‐care systems in smart home

Increasing level of personal and social health and life expectancy has resulted in the growth of aged people population. Elderly care is an essential and costly issue in any society that should be addressed in researches. Elderly care has faced some problems such as elderly solitary, trained caregivers insufficiency, and increasing cost of late diagnosis of diseases and accident. Regarding to these problems, smart home technology can be used as an efficient solution. It provides an expertise, long‐term, and low‐cost care that empowers elderly to have an independent life. In this paper, we propose an architecture for a health‐care system in a smart home. In this architecture, rapid and timely diagnosis of environmental incidents and health risks causes reduction in costs of health care and relief. Given to the vital aspect of health‐care systems, the proposed architecture components and solutions are presented to meet quality attributes such as availability, performance, security, and interoperability. The proposed architecture evaluation is based on the Architecture Tradeoff Analysis Method (ATAM) scenario‐based approach. ATAM is a method for analysis and evaluation of software systems architecture. And quality attributes scenarios are examined to meet quality requirement. Compared with the previous works, more quality attributes are addressed in this paper.     

A novel architecture of Proxy‐LMA mobility management scheme for software‐based smart factory networking

Mobile users expect a network service, in which seamless handoff occurs while moving on a next generation wireless network. In addition, in smart factories (SFs), communication is required between factory floor and manufacturing zone, as well as connectivity towards office IT, or remote production facilities that are connected via wide area network or internet. For this purpose, interworking between heterogeneous networks is important, but there has been little research on global mobility support. Therefore, this paper proposes Proxy‐LMA technology, a mobile IP‐based global internetworking system, to improve global mobility and interoperability in the SFs network environment. The purpose of the proposed Proxy‐LMA system is to support global mobility by using mobility management protocols such as PMIPv6 and MIPv6 in heterogeneous network environment. As a result of the performance evaluation, Proxy‐LMA system is more efficient than other methods in terms of signaling cost and response delay in heterogeneous network environment. Software‐based networking in SFs enables them to easily adapt the communication network to changing requirements. Similar to cloud‐based systems, such SFs could be seen as production clusters that could be rented and configured as needed. The SF network uses software‐defined networking combined with network functions virtualization, to achieve the required flexibility. Despite the fact that the technology is nowadays not yet ready for deployment in today's manufacturing networks, a novel network architecture for SFs based on software‐defined networking and network virtualization is here proposed, to support smart services, especially for Industrie 4.0.     

A middleware approach for reliable resource selection on Internet‐of‐Things

Decision making plays a vital role in the selection of resources so that they actively participate for communication and computation on the Internet‐of‐Things platform. For the same, they require the elimination of the challenges related to knowledge representation, discovery, trust, and security due to continuously changing mobility patterns, heterogeneity, interoperability, and scalability on the network. To address the challenges, a novel three‐layered approach, namely, middleware approach for reliable resource selection on Internet‐of‐Things (MARRS‐IoT), is proposed. It performs a search through neighbor discovery algorithm and evaluates trust score of the discovered resources, both locally and globally using fuzzy‐decision algorithm and performs efficient communication among resources via hybrid M‐gear protocol. The approach is simulated and compared against algorithms, namely, particle swarm optimization, ants colony optimization, and binary genetic to evaluate its performance. The obtained results support the efficacy of the MARRS‐IoT with respect to throughput and execution time.     

A novel chain-based clustering for green communication in wireless sensor network

The rapid growth of the Internet of Things (IoT) creates a high requirement for data collected through wireless sensor networks (WSNs), resulting in a lot of emphasis on WSN data collecting in recent decades. However, the resource-constrained nature of sensor devices exerts heavy constraints for acquiring the optimal network performance. To resolve this concern, this paper proposes the Chain-based Energy-Efficient Clustering (CBEEC) Routing Protocol that considers nodes of two heterogeneous levels of energy (normal level and advanced level). The number of advanced nodes in CBEEC is analytically determined and hence are analytically allocated. However, normal nodes are stochastically deployed in the vicinity of advanced nodes. The data transmission is performed among the advanced nodes in the form of chain and from there it is forwarded to base station (BS). Consequently, it preserves immense amount of energy for the network. The performance of CBEEC is empirically investigated with benchmarks of different performance metrics and simulation outcomes showing that the CBEEC outperforms state-of-the-art routing protocols.     

Comparison of self‐adaptive dynamic differential evolution and particle swarm optimization for smart antennas in wireless communication

In this paper, ultrawide band (UWB) communication systems with eight transmitting and receiving ring antenna arrays are implemented to test the bit error rate and capacity performance. By using the ray‐tracing technique to compute any given indoor wireless environment, the impulse response of the system can be calculated. The synthesized beamforming problem can be reformulated into a multiobjective optimization problem. Self‐adaptive dynamic differential evolution (SADDE) and particle swarm optimization (PSO) are used to find the excitation current and the feed line length of each antenna to form the appropriate beam pattern. This pattern can then reduce the bit error rate and increase the channel capacity and receiving energy. Numerical results show that the fitness value and the convergence speed by the SADDE are better than those by the PSO. Moreover, the SADDE had better results for both line‐of‐sight and nonline‐of‐sight cases. In other words, compared with PSO, SADDE has improved more effectively the main beam radiation energy and reduced the multipath interference.     

Single threshold optimization and a novel double threshold scheme for non‐line‐of‐sight identification

Non‐line‐of‐sight (NLOS) identification is a critical issue in wireless location system. The traditional method only considers the probability of false alarm to obtain the single threshold to identify the wireless propagation environment as line of sight (LOS) or NLOS. However, the probability of missed detection is rarely discussed. In this paper, firstly, the optimal single threshold is derived through minimizing the total error probability, which is the sum of the probability of false alarm and the probability of missed detection. Further, if the prior probabilities for the LOS and NLOS environment are available, a novel double threshold method based on the optimal single threshold is proposed to perform NLOS identification. By comparing the total error probability, we show through simulation results that the performance of the optimal single threshold is better than that of the traditional single threshold. Moreover, the novel double threshold NLOS identification scheme is feasible and better than the optimal single threshold scheme. It is also shown that the analytical results are consistent with the simulated ones.Copyright © 2012 John Wiley & Sons, Ltd.     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.     

SRGH: A secure and robust group‐based handover AKA protocol for MTC in LTE‐A networks

Machine‐type communication (MTC) is defined as an automatic aggregation, processing, and exchange of information among intelligent devices without humans intervention. With the development of immense embedded devices, MTC is emerging as the leading communication technology for a wide range of applications and services in the Internet of Things (IoT). For achieving the reliability and to fulfill the security requirements of IoT‐based applications, researchers have proposed some group‐based handover authentication and key agreement (AKA) protocols for mass MTCDs in LTE‐A networks. However, the realization of secure handover authentication for the group of MTCDs in IoT enabled LTE‐A network is an imminent issue. Whenever mass MTCDs enter into the coverage area of target base‐station simultaneously, the protocols incur high signaling congestion. In addition, the existing group‐based handover protocols suffer from the huge network overhead and numerous identified problems such as lack of key forward/backward secrecy, privacy‐preservation. Moreover, the protocols fail to avoid the key escrow problem and vulnerable to malicious attacks. To overcome these issues, we propose a secure and robust group‐based handover (SRGH) AKA protocol for mass MTCDs in LTE‐A network. The protocol establishes the group key update mechanism with forward/backward secrecy. The formal security proof demonstrates that the protocol achieves all the security properties including session key secrecy and data integrity. Furthermore, the formal verification using the AVISPA tool shows the correctness and informal analysis discusses the resistance from various security problems. The performance evaluation illustrates that the proposed protocol obtains substantial efficiency compared with the existing group‐based handover AKA protocols.     

提高WCDMA单用户吞吐率的优化方法

本文主要对大学城周边WCDMA数据业务进行了一些优化,通过对现网数据进行分析,在未达到负荷上限的小区,通过优化信道类型切换参数和RLC参数,提高单MAC用户吞吐率,提高服务质量,又提升了用户感知,达到提高网络资源利用率的目的。并就优化前后进行比较,总结出提升网络资源利用率的经验。     

AID‐based backoff for throughput enhancement in 802.11ah networks

IEEE 802.11ah is a WiFi standard developed to address requirements associated with the heterogeneous nature of the Internet of Things. It uses the sub‐1 GHz bands to enable long‐range transmissions for low‐power devices. However, long‐range coverage exacerbates the hidden node problem. To mitigate this problem, sectorization and grouping have been proposed by the standard. In this paper, hierarchical clustering and a modified Welsh‐Powell algorithm are combined to create hidden node‐free groups in a sectorized wireless local area network. An analytical model is developed for a deterministic backoff technique that uses association identifiers to set the backoff in order to eliminate collisions. The performance of the deterministic backoff technique is compared with that of existing schemes. Our results show that this approach results in higher throughput and lower latency than other deterministic backoff techniques.