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Group communication enables Internet of Things (IoT) devices to communicate in an efficient and fast manner. In most instances, a group message needs to be encrypted using a cryptographic key that only devices in the group know. In this paper, we address the problem of establishing such a key using a lattice-based one-way function, which can easily be inverted using a suitably designed lattice trapdoor. Using the notion of a bad/good basis, we present a new method of coupling multiple private keys into a single public key, which is then used for encrypting a group message. The protocol has the apparent advantage of having a conjectured resistance against potential quantum-computer-based attacks. All functions—key establishment, session key update, node addition, encryption, and decryption—are effected in constant time, using simple linear-algebra operations, making the protocol suitable for resource-constrained IoT networks. We show how a cryptographic session group key can be constructed on the fly by a user with legitimate credentials, making node-capture-type attacks impractical. The protocol also incorporates a mechanism for node addition and session-key generation in a forward- and backward-secrecy-preserving manner. [ABSTRACT FROM AUTHOR]

Energy efficiency is an important quality-of-service requirement that needs to be considered when designing an efficient MAC protocol for a WBAN system due to the limited power resources of biomedical sensor devices. To address this, an energy-aware multi-group hybrid MAC (MG-HYMAC) protocol is proposed in this work to improve energy efficiency as well as the lifetime of the biomedical sensor devices in a personalized healthcare system. The proposed protocol combines both the advantages of the CSMA/CA and the TDMA schemes to enable the biomedical sensors to efficiently contend for transmission opportunities and to allow them to efficiently transmit health data. The MG-HYMAC protocol is combined with a transmission scheduling technique to duty cycle the operations of the biomedical devices with less critical data to determine when and how the biomedical sensor devices will transmit their health data packets in order to reduce collisions to save energy and prolong the battery lifetime of the biomedical sensor devices so as to improve the overall network lifetime. Also, a stochastic probability model and a heuristic-based power control scheme are developed to solve time allocation and power control problems to improve energy efficiency and the biomedical sensor devices lifetime. To validate the MG-HYMAC protocol, it was compared with other related protocols (including HyMAC and CPMAC) and simulated in MATLAB. The simulation results proved that the proposed MG-HYMAC protocol outperformed the existing MAC protocols using standard metrics like energy efficiency, biomedical sensor devices lifetime, and convergence speed. [ABSTRACT FROM AUTHOR]

Summary: The Constrained Application Protocol (CoAP) is a lightweight web transfer protocol designed based on the REST architecture standardized by the Internet Engineering Task Force (IETF) to meet and accommodate the requirements of the constrained Internet of Things (IoT) environments. Managing congestion control in a resource‐constrained lossy network with a high bit error rate is a significantly challenging task that needs to be addressed. The primary congestion control mechanism defined by CoAP specification leverages on basic binary exponential backoff and often fails to utilize the network dynamics to the best of its traffic conditions. As a result, CoCoA has been introduced for better IoT resource utilization. In addition, CoCoA retransmission timeout (RTO) for network dynamics is based on constant coefficient values. The resource‐constrained nature of IoT networks poses new design challenges for congestion control mechanisms. In this paper, we propose a new particle swarm optimization (PSO)‐based congestion control approach called psoCoCoA as a variation of CoCoA. The psoCoCoA applies random and optimal parameter‐driven simulation to optimize default CoAP parameters and update the fitness and velocity positions to adapt to the traffic conditions. This process is performed for different traffic scenarios by varying the retransmission and max‐age values by using the optimization‐based algorithm. We carried out extensive simulations to validate the congestion control performance for CoAP with Observe, CoCoA, and psoCoCoA with different network topologies. The results indicate that psoCoCA outperforms or very similar to CoCoA and achieves better performance compared to CoAP with Observe under different network scenarios. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) is a concept that is currently gaining a lot of popularity as a result of its potential to be incorporated into many heterogeneous systems. Because of its diversity, integrating IoT is conceivable in almost all fields, including the healthcare sector. For instance, a promising technology in the healthcare sector known as wireless body area network (WBAN) could be integrated with the IoT to enhance its productivity. However, in order to guarantee the optimization of the operation of the healthcare applications facilitated by the WBAN-enabled IoT technology, there must be enough support from all the different protocol stack layers so as to satisfy the critical quality-of-service (QoS) requirements of the WBAN systems. Consequently, the medium access control (MAC) protocol has recently been gaining lots of attention in the area of WBANs due to its ability to manage and coordinate when a shared communication channel can be accessed. For the purpose of achieving efficient MAC protocols for WBAN-enabled IoT technology, this paper investigates some key MAC protocols that could be exploited in WBANs based on their characteristics, service specifications, technical issues such as energy wastage issues, and possible technical solutions were provided to enhance energy efficiency, channel utilization, data transmission rate, and dealy rate. Also, these MAC protocols were grouped and compared based on short- and long-range communication standards. Following this, future directions and open research issues are pointed out. [ABSTRACT FROM AUTHOR]

This study proposes a hybrid MAC protocol that can efficiently and effectively optimize the communication channel access of a WBAN multi-class system. The proposed protocol consists of two major processes that include the contention phase (CP) and the transmission phase (TP). In the CP, only the biomedical devices that have health packets to transmit randomly contend with equal probabilities using a slotted ALOHA scheme for transmission opportunities and the successful biomedical devices are allocated a transmission time-slot by employing a reservation-based time division multiple access (TDMA) scheme in the transmission phase. A multi-objective optimization problem was formulated to maximize the system sum-throughput, packet success-access-ratio, as well as the reservation ratio, and solved by the controller (i.e., access point) to determine the optimal length of the CP and the number of biomedical devices that can transmit in the TP. Monte Carlo simulation was performed and the optimization solution improved the proposed protocol’s performances. For validation purposes, the simulated results in MATLAB revealed that the proposed protocol performs better than the contemporary system in the context of the system sum-throughput, reservation ratio, and the average health packet delay with performance gains of about 9.2%, 9.5%, and 9.6% respectively. [ABSTRACT FROM AUTHOR]

Some recent survey statistics suggest that low power wide area networks (LPWANs) are fast becoming the most prevalent communication platform used in many applications of the Internet of Things (IoT). However, because most LPWANs are generally deployed in the presently congested industrial, scientific, and medical bands, they are invariably plagued by problems associated with spectral congestion, such as increased interference, reduced data rates, and spectra inefficiency. These problems are solvable by integrating cognitive radio (CR) technologies in LPWAN (termed CR-LPWAN), for which some pioneering solutions now exist in the literature. Consequently, this article takes an early look at some of these pioneering efforts pertaining to the development of CR-LPWAN systems. We discuss a general network architecture and a physical layer front-end model suitable for CR-LPWAN systems. Then, some notable state-of-the-art approaches for CR-LPWAN systems are discussed. Potential advantages of CR-LPWAN systems for IoT-based applications are also presented, and this article closes with a few research challenges and future research directions in this regard. This article aims to serve as a starting point for most budding researchers who may be interested in the development of effective and efficient CR-LPWAN systems for the enhancement of different IoT-based applications. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) promises a wide range of application benefits to the Internet of Things (IoT) including the flexible management of wireless sensor networks (WSNs). While the integration of SDN techniques in WSNs is being extensively investigated, there remains a need for a general SDN-based management system for WSNs. A system that should provide an opportunity for rapid testing and implementation of management modules to the IoT developer community working on taking advantage of the SDN benefits. Therefore, this paper proposes a generic and modular WSN management system based on SDN (SDNMM). SDNMM introduces the concept of management modularity using a management service interface (MSI) that enables management entities to be added as modules. The system leverages the use of SDN in WSNs and by being modular it also allows for rapid development and implementation of IoT applications. The system has been built on an open source platform to support its generic aspect and a sample resource management module implemented and evaluated to support the proposed modular management approach. Results show how adding a resource management module via the MSI improved packet delivery, delay, control traffic, and energy consumption over comparable frameworks. [ABSTRACT FROM AUTHOR]

With the emerging applications of the Internet of Things (IoT), a congestion control mechanism becomes a critical phenomenon for efficient communication in networks of constrained devices. The Internet Engineering Task Force developed the constrained application protocol (CoAP) as a standard communication protocol that favors lightweight interoperability for accommodating resource‐constrained devices. However, the base CoAP specification congestion control is insensitive to various network conditions. Thus, differentiating the scenario of packet loss due to bit error rate and congestion, and identifying correct round trip time (RTT) of retransmitted message‐acknowledgement is quite essential to adapt the CoAP behavior based on the network status. In this paper, we present a context‐aware congestion control (CACC) approach for lightweight CoAP/user datagram protocol–based IoT traffic. The CACC proposes mechanisms that include retransmission timeout (RTO) estimator, retransmission count–based smoothed round‐trip‐time observation, lower bound RTO restriction approach, and aging concept. The proposed RTO estimators utilize the strong, weak, and failed RTT to identify exact network status and provide adaptive congestion control. The CACC incorporates the variable of retransmission count in request‐response interaction model to mitigate the negative variation in RTT due to the fluctuation in the IoT environment. Moreover, with lower bound RTO restriction approach, the unnecessary spurious retransmissions are avoided, and the aging mechanism limits the validity of the RTO value to improve the efficiency of the proposed scheme. The proposed model is validated against baseline CoAP and CoCoA+ using Contiki OS and the Cooja simulator. The results are impressive under different network topologies. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management. [ABSTRACT FROM AUTHOR]

Industrial wireless sensor networks (IWSNs) have to contend with environments that are usually harsh and time-varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilized by many other wireless technologies. With wireless applications rapidly growing, it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and coexisting with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio (CR) methods offer a potential solution to improve resistance of IWSNs to interference. Integrating CR principles into the lower layers of IWSNs can enable devices to detect and avoid interference, and potentially opens the possibility of utilizing free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarize CR methods relevant to industrial applications, covering CR architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access (DSA), game theory, and CR network (CRN) security. [ABSTRACT FROM PUBLISHER]

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm that enables many beneficial and prospective application areas, such as smart metering, smart homes, smart industries, and smart city architectures, to name but a few. These application areas typically comprise end nodes and gateways that are often interconnected by low power wide area network (LPWAN) technologies, which provide low power consumption rates to elongate the battery lifetimes of end nodes, low IoT device development/purchasing costs, long transmission range, and increased scalability, albeit at low data rates. However, most LPWAN technologies are often confronted with a number of physical (PHY) layer challenges, including increased interference, spectral inefficiency, and/or low data rates for which cognitive radio (CR), being a predominantly PHY layer solution, suffices as a potential solution. Consequently, in this article, we survey the potentials of integrating CR in LPWAN for IoT-based applications. First, we present and discuss a detailed list of different state-of-the-art LPWAN technologies; we summarize the most recent LPWAN standardization bodies, alliances, and consortia while emphasizing their disposition towards the integration of CR in LPWAN. We then highlight the concept of CR in LPWAN via a PHY-layer front-end model and discuss the benefits of CR-LPWAN for IoT applications. A number of research challenges and future directions are also presented. This article aims to provide a unique and holistic overview of CR in LPWAN with the intention of emphasizing its potential benefits. [ABSTRACT FROM AUTHOR]

Long-Range Wide Area Network (LoRaWAN) is a fast-growing communication system for Low Power Wide Area Networks (LPWAN) in the Internet of Things (IoTs) deployments. LoRaWAN is built to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRaWAN employs an Adaptive Data Rate (ADR) scheme that dynamically optimizes data rate, airtime, and energy consumption. The major challenge in LoRaWAN is that the LoRa specification does not state how the network server must command end nodes pertaining rate adaptation. As a result, numerous ADR schemes have been proposed to cater for the many applications of IoT technology, the quality of service requirements, different metrics, and radio frequency (RF) conditions. This offers a challenge for the reliability and suitability of these schemes. This paper presents a comprehensive review of the research on ADR algorithms for LoRaWAN technology. First, we provide an overview of LoRaWAN network performance that has been explored and documented in the literature and then focus on recent solutions for ADR as an optimization approach to improve throughput, energy efficiency and scalability. We then distinguish the approaches used, highlight their strengths and drawbacks, and provide a comparison of these approaches. Finally, we identify some research gaps and future directions. [ABSTRACT FROM AUTHOR]

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard. [ABSTRACT FROM AUTHOR]

Software‐defined wireless sensor networking (SDWSN) is a new wireless networking paradigm formed by applying software‐defined networking to wireless sensor networks. Fragmentation‐based distributed control system offers an efficient way of distributing the SDWSN control services across the network. Fragmentation aims to bring the control services closer to the infrastructure network in order to reduce the propagation latency. It also aims to improve the scalability, reliability, and performance of the network. The fragmentation model is earmarked to play a huge role in stimulating participation of SDWSN in Internet of Things. To realize this, we optimize the model for deployment and integration as well as for operational efficiency. We consider two aspects: controller placement and controller reelection after failure. This paper discusses the controller placement techniques suitable for SDWSN and the controller replacement in a case of failure for SDWSN. The controller placement and the controller replacement mechanism were both evaluated and the results proved to be effective and efficient. [ABSTRACT FROM AUTHOR]

The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches. [ABSTRACT FROM AUTHOR]

In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field. [ABSTRACT FROM AUTHOR]