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Using IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the Internet of Things (IoT): Problem Statement
... During each slotframe cycle, the channel number changes according to the predefined channel hopping schema. The release of RFC 7554 [30] prompted the 6TiSCH initiative to enhance communication over IEEE 802.15.4, leading to the specification of 6TiSCH Operation Sublayer Protocol (6P) in RFC 8480 [31] and the more recent release of RFC 9033 [32], which includes the MSF specification. ...
... Although the standard does not define the TSCH schedule, the informational RFC 7554 [30] guides by highlighting issues and goals for further research and improvements. Since then, the 6TiSCH initiative has significantly improved its attention to the scheduling schema. ...
... According to Masirap et al. [45], wireless networks are generally considered less reliable than wired connections. However, RFC7554 [30] highlights that the TSCH medium access technique, which combines time synchronisation and channel hopping, contributes significantly to improve reliability. However, reliability is of significant functional importance in industrial environments. ...
Time-Slotted Channel Hopping (TSCH) Media Access Control (MAC) has become a leading wireless technology for industrial applications, offering deterministic communication while balancing latency, bandwidth, and energy consumption. This study addresses the critical challenge of cell scheduling within TSCH MAC, emphasising the importance of selecting scheduling mechanisms based on application-specific quality of service parameters. Despite numerous proposals and evaluations, the lack of standardised scheduling methods and comprehensive performance metrics remains a significant obstacle. Traditional network metrics often fail to capture key issues in TSCH-based mesh networks, potentially overlooking indicators of network stability. To address this gap, we examine both application and network metrics from a mesh network perspective and propose a set of complementary metrics tailored to the characteristics of TSCH. These metrics provide a more detailed evaluation of how scheduling impacts network reliability and efficiency. Given the diverse applications and configurations in industrial environments, this study offers insights into employing these complementary metrics for a more accurate assessment of the impact of TSCH scheduling. Ultimately, our approach aims to improve TSCH scheduling evaluation and contribute to advancing industrial wireless communication systems.
... Although it considers the orchestration and optimization of the performance parameters, it overlooks the number of collisions during communication. The TSCH protocol, standardized by IEEE 802.15.4-2015 [8], offers a robust solution for low-power and lossy networks (LLNs). This link layer protocol ensures high reliability, minimal current consumption, and low latency. ...
... TSCH divides time into distinct time slots. These time slots are lengthy enough to allow for both the transmission of a packet and the receipt of acknowledgment [8]. A slotframe is a sequence of time slots that repeats itself throughout time in a cyclic fashion. ...
... Hopping Sequence TSCH utilizes 16 radio channels [8,29]. This multi-channel setup [39] enables nodes to dynamically switch between channels, offering enhanced reliability by mitigating the effects of multi-path fading and external interference [40]. ...
The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off.
... However, it also means that some extra time has to be reserved in the scheduling matrix for these redundant paths [9], which may be used only if a failure occurs. Alternatively, additional cells may be provisioned for the retransmissions to increase the network's reliability [10,11]. In this case, the awake time of nodes increases further, as well as the network's required resources and power consumption. ...
... More precisely, a cell is defined in the scheduling matrix by a time slot and a channel offset. This channel offset is then derived into a frequency at the beginning of the time slot according to the Equation (1) [10]: ...
... The links on the path to the root are calculated (lines[8][9][10][11][12]. getParent function returns all parents of node, and algorithm 2 performa a kind of breadth first search (BFS).(3) ...
The Industrial Internet of Things (IIoT) has emerged as a technology that automates industrial processes. In IIoT networks, data are collected from various nodes and sent to a base station for managerial purposes. However, in the industrial environment, network reliability and delay are significant challenges due to the high likelihood of packet loss in radio networks. Anycast is a link layer mechanism that increases reliability and reduces delay by allowing multiple receivers to be connected to a sender, and a single packet is simultaneously sent to all receivers. The receivers decode the packet based on their priorities, and transmission succeeds if at least one receiver can decode the packet. Moreover, mechanisms exist to limit the number of duplicates. This paper proposes a novel centralized anycast aware scheduling algorithm (AASA), which implements anycast based on the 802.15.4e-time-slotted channel hopping (TSCH) standard and in the stack of the 6TiSCH protocol. The goal of AASA is to improve IIoT networks more reliable and reduce end-to-end delay. To do this, upon a link failure, AASA chooses an alternative link and the packet is sent without any delay via that link through the same time slot. We implemented AASA in 6TiSCH simulator and carried out different scenarios to investigate its efficiency under various conditions. Results from simulations show that AASA effectively increases reliability by reducing repetitive packet transmissions and, thus, decreasing the delay in packet delivery.
... After the RFC 7554 [26] release, the 6TiSCH rises as an effort to enhance communication over IEEE 802.15.4. In addition, the RFC 8180 [23] defined functional parameters and guidelines for a minimal operation mode for building a 6TiSCH network. ...
... RFC 7554 [26] summarises the issues and goals that have been used as a guideline for further research and development of the challenges which need to be dealt with while using RPL on top of TSCH. Notwithstanding, the TSCH standard did not define a schedule schema. ...
... During the bootstrap phase, the LBR self propagates as a time source and starts to announce the presence of the network sending EB frames. Each candidate node joining the network must listen for EB for getting the current ASN, information about the slotframes and timeslots, and a 1-byte join priority [26]. After network synchronisation, the ingressed node sends EB frames to assist nearpledging nodes in ingressing the network. ...
Low-power and Lossy Networks (LLN) are utilised for numerous Internet of Things (IoT) applications. IEEE has specified the Time-slotted Channel Hopping (TSCH) Media Access Control (MAC) to target the needs of Industrial IoT. TSCH supports deterministic communications over unreliable wireless environments and balances energy, bandwidth and latency. Furthermore, the Minimal 6TiSCH configuration defined Routing Protocol for Low power and Lossy networks (RPL) with the Objective Function 0 (OF0). Inherent factors from RPL operation, such as joining procedure, parent switching, and trickle timer fluctuations, may introduce overhead and overload the network with control messages. The application and RPL control data may lead to an unpredicted networking bottleneck, potentially causing network instability. Hence, a stable RPL operation contributes to a healthy TSCH operation. In this paper, we explore TSCH MAC and RPL metrics to identify factors that lead to performance degradation and specify indicators to anticipate network disorders towards increasing Industrial IoT reliability. A TSCH Schedule Function might employ the identified aspects to foresee disturbances, proactively allocate the proper amount of cells, and avoid networking congestion.
... After the RFC 7554 [26] release, the 6TiSCH rises as an effort to enhance communication over IEEE 802.15.4. Finally, the 6TiSCH Operation Sublayer (6top) Protocol (6P) was specified by RFC 8480 [27], and the RFC 9033 [28] was released with Minimal Scheduling Function (MSF) specification, resulting in the stack illustrates in Figure 1. ...
... Since then, the 6TiSCH has been the focus of various improvements, and we would point out the scheduling schema that has received a lot of attention. The TSCH schedule was not defined by the standard, but the informational RFC 7554 [26] cites issues and goals that have been employed as a guideline for research and improvements. Although RFC 8180 [29] defined an IPv6 minimal working mode over TSCH, the minimal schema is not enough for reaching some critical requirements. ...
... As cited before, during the bootstrap phase, the LLN Border Router (LBR) self propagates as a time source and starts to announce the presence of the network, sending EB frames. Each candidate node joining the network must listen for EB for getting the current Absolute Slot Number (ASN), information about the slot-frames and timeslots, and a 1-byte join priority [26]. After network synchronisation, the ingressed node joins the RPL network, computes its rank, and sends EB frames to assist near-pledging nodes in ingressing the network. ...
The support of critical industrial applications using constrained sensing devices will require deterministic communication technologies, which has motivated the design of TSCH media access control mechanisms. TSCH carefully manages the necessary energy to support deterministic communications while trying to cope with the bandwidth and latency required for the application. Nevertheless, using TSCH to support Industrial critical applications is still challenging since scheduling strategies lack the elasticity to cope with dynamic and error-prone communication environments. Also, scheduling adaptation strategies may be designed to benefit from the information in other layers. We propose a distributed TSCH scheduling based on analysing network behaviour, particularly employing the RPL Trickle algorithm to support dynamic and network-aware scheduling. The proposed strategy contributes to anticipating disturbances and the network’s overall stability. The proposal was evaluated for its performance at the application and MAC layers, and we may observe that it contributes to increasing network efficiency.
... The IEEE 802.15.4e standard [7] defines the physical and the medium access (MAC) layers for LLNs. There are five MAC modes, including Time Slotted Channel Hopping (TSCH) [19]. TSCH combines channel hopping and time synchronization where all nodes in the network follow a common schedule that specifies for each node on which channel and at which time slot to communicate with its neighbors. ...
... Each cell is a pair of slot and channel offset coordinates assigned to a given link. The slot offset is equal to time slot t while the channel offset chOf is translated into a frequency using a function defined in the standard [19]. The number of channel offsets is equal to the number of available frequencies 0 ≤ chOf < M . ...
The recently created IETF 6TiSCH working group combines the high reliability and low-energy consumption of IEEE 802.15.4e Time Slotted Channel Hopping with IPv6 for industrial Internet of Things. We propose a distributed link scheduling algorithm, called Local Voting, for 6TiSCH networks that adapts the schedule to the network conditions. The algorithm tries to equalize the link load (defined as the ratio of the queue length over the number of allocated cells) through cell reallocation. Local Voting calculates the number of cells to be added or released by the 6TiSCH Operation Sublayer (6top). Compared to a representative algorithm from the literature, Local Voting provides simultaneously high reliability and low end-to-end latency while consuming significantly less energy. Its performance has been examined and compared to On-the-fly algorithm in 6TiSCH simulator by modeling an industrial environment with 50 sensors.
... TSCH is a globally synchronized network where traffic is transmitted based on a static cyclic schedule table called slotframe C that repeats with the period equal to the slotframe size |C| [11], [12]. In a TSCH network, the slotframe (schedule) is divided into equal-length timeslots as shown in Fig. 1. ...
... To calculate the reliability, we first define the Packet Delivery Ratio (PDR) during a cycle in Eq. (12). The control plane performs the calculation of the PDR as follows. ...
p>The Industrial Internet of Things (IIoT) is shaping the next generation of cyber-physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a Reinforcement Learning (RL) framework to optimize the slotframe size of the Time Slotted Channel Hopping (TSCH) protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user’s requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.</p
... TSCH is a globally synchronized network where traffic is transmitted based on a static cyclic schedule table called slotframe C that repeats with the period equal to the slotframe size |C| [13], [14]. In a TSCH network, the slotframe (schedule) is divided into equal-length timeslots as shown in Fig. 1. ...
... To this end, we define the power dependability P DEP of the node ν i ∈ V in Eq. (14). The power dependability of a node shows how much the node is under the workload, i.e., transmission and receiving the data. ...
p>The Industrial Internet of Things (IIoT) is shaping the next generation of cyber-physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a Reinforcement Learning (RL) framework to optimize the slotframe size of the Time Slotted Channel Hopping (TSCH) protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user’s requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.</p
... TSCH is a globally synchronized network where traffic is transmitted based on a static cyclic schedule table called slotframe C that repeats with the period equal to the slotframe size |C| [13], [14]. In a TSCH network, the slotframe (schedule) is divided into equal-length timeslots as shown in Fig. 1. ...
... To this end, we define the power dependability P DEP of the node ν i ∈ V in Eq. (14). The power dependability of a node shows how much the node is under the workload, i.e., transmission and receiving the data. ...
p>The Industrial Internet of Things (IIoT) is shaping the next generation of cyber-physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a Reinforcement Learning (RL) framework to optimize the slotframe size of the Time Slotted Channel Hopping (TSCH) protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user’s requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.</p
... Wireless communications are currently employed in many different contexts. Besides personal connectivity and office/home environments, they are becoming increasingly popular also in other scenarios, like intelligent transportation systems [1], Internet of Things (IoT) [2], environmental monitoring [3], and precision agriculture [4], to cite a few. Although wireless networks have the potential to bring tangible benefits also to time-sensitive applications, they are quite unreliable and scarcely deterministic, and so are often deemed unsuitable in contexts like factory automation. ...
... Medium Access Control (MAC) mechanism are intertwined in order to improve performance further. In particular, specific duplication avoidance (DA) mechanisms are introduced to prevent transmission on air of identical copies of the same packet, when doing so 2 is useless, so as to reduce network load. Basically, Wi-Red can be seen as an holistic approach aimed at optimizing time and frequency diversity in Wi-Fi. ...
p>Seamless redundancy layered atop Wi-Fi has been shown able to tangibly increase communication quality, hence offering industry-grade reliability. However, it also implies much higher network traffic, which is often unbearable as the wireless spectrum is a shared and scarce resource. To deal with this drawback the Wi-Red proposal includes suitable duplication avoidance mechanisms, which reduce spectrum consumption by preventing transmission on air of inessential frame duplicates.
In this paper, the ability of such mechanisms to save wireless bandwidth is experimentally evaluated. To this purpose, specific post-analysis techniques have been defined, which permit to carry out such an assessment on a simple testbed that relies on plain redundancy and do not require any changes to the adapters' firmware. As results show, spectrum consumption decreases noticeably without communication quality is impaired. Further saving can be obtained if a slight worsening is tolerated for latencies.</p
... Wireless communications are currently employed in many different contexts. Besides personal connectivity and office/home environments, they are becoming increasingly popular also in other scenarios, like intelligent transportation systems [1], Internet of Things (IoT) [2], environmental monitoring [3], and precision agriculture [4], to cite a few. Although wireless networks have the potential to bring tangible benefits also to time-sensitive applications, they are quite unreliable and scarcely deterministic, and so are often deemed unsuitable in contexts like factory automation. ...
... Medium Access Control (MAC) mechanism are intertwined in order to improve performance further. In particular, specific duplication avoidance (DA) mechanisms are introduced to prevent transmission on air of identical copies of the same packet, when doing so 2 is useless, so as to reduce network load. Basically, Wi-Red can be seen as an holistic approach aimed at optimizing time and frequency diversity in Wi-Fi. ...
p>Seamless redundancy layered atop Wi-Fi has been shown able to tangibly increase communication quality, hence offering industry-grade reliability. However, it also implies much higher network traffic, which is often unbearable as the wireless spectrum is a shared and scarce resource. To deal with this drawback the Wi-Red proposal includes suitable duplication avoidance mechanisms, which reduce spectrum consumption by preventing transmission on air of inessential frame duplicates.
In this paper, the ability of such mechanisms to save wireless bandwidth is experimentally evaluated. To this purpose, specific post-analysis techniques have been defined, which permit to carry out such an assessment on a simple testbed that relies on plain redundancy and do not require any changes to the adapters' firmware. As results show, spectrum consumption decreases noticeably without communication quality is impaired. Further saving can be obtained if a slight worsening is tolerated for latencies.</p
... Wireless communications are currently employed in many different contexts. Besides personal connectivity and office/home environments, they are becoming increasingly popular also in other scenarios, like intelligent transportation systems [1], Internet of Things (IoT) [2], environmental monitoring [3], and precision agriculture [4], to cite a few. Although wireless networks have the potential to bring tangible benefits also to time-sensitive applications, they are quite unreliable and scarcely deterministic, and so are often deemed unsuitable in contexts like factory automation. ...
... Medium Access Control (MAC) mechanism are intertwined in order to improve performance further. In particular, specific duplication avoidance (DA) mechanisms are introduced to prevent transmission on air of identical copies of the same packet, when doing so 2 is useless, so as to reduce network load. Basically, Wi-Red can be seen as an holistic approach aimed at optimizing time and frequency diversity in Wi-Fi. ...
Seamless redundancy layered atop Wi-Fi has been shown able to tangibly increase communication quality, hence offering industry-grade reliability. However, it also implies much higher network traffic, which is often unbearable as the wireless spectrum is a shared and scarce resource. To deal with this drawback the Wi-Red proposal includes suitable duplication avoidance mechanisms, which reduce spectrum consumption by preventing transmission on air of inessential frame duplicates. In this paper, the ability of such mechanisms to save wireless bandwidth is experimentally evaluated. To this purpose, specific post-analysis techniques have been defined, which permit to carry out such an assessment on a simple testbed that relies on plain redundancy and do not require any changes to the adapters' firmware. As results show, spectrum consumption decreases noticeably without communication quality is impaired. Further saving can be obtained if a slight worsening is tolerated for latencies.
... During every slot-frame cycle, the channel number changes according to the channel hopping schema defined by the standard. After the RFC 7554 [4] release, the 6TiSCH rises as an effort for enhancing the communication over IEEE 802. 15.4. ...
... The TSCH schedule was not defined by the standard, but the informational RFC 7554 [4] mentions issues and goals that have been utilised as a guideline for investigation and advancements. Since then, the focus has been put on scheduling techniques. ...
Composed of constrained devices, Low-Power and Lossy Networks (LLNs) have been applied for numerous Internet of Things (IoT) applications. In addition, the Time-Slotted Channel Hopping (TSCH) media access control has been specified by IEEE aiming at Industrial IoT (IIoT). TSCH brings in the deterministic factor over wireless communication; it balances energy, bandwidth and latency, offering reliable communication. This paper conducted an experiment to reach the Contiki-NG’s TSCH Minimal Schedule breaking point. We analysed network factors to identify evidence that may lead to performance degradation. The located evidence might be employed by edge resilient counter-measures systems developers, vertical integration researchers or scheduling function designers to improve systems and increase the IIoT reliability.
... Time Slotted Channel Hopping is the MAC protocol defined in IEEE 802.15.4e [5] enabling parallel communications on different channels within the same timeslot. It has been proposed to meet the requirements of industrial applications. ...
... TSCH uses slot frames to provide synchronized communications to the network. A slot frame consists of a group of time slots, continually repeated over time; a Time slot is a short time interval that is sufficient to perform the transmission of a data frame and an ASK [5]. Within a slot frame, each node receives indications as to what to do in each slot: transmit (TX), receive (RX), or sleep. ...
The Industrial Internet of Things (IIoT) connects a large number of industrial objects to the Internet that requires a higher level of control in terms of reliability, low power, and delay. IEEE 802.15.4e is the standard of the IIoT and includes time-synchronized channel hopping mechanisms to allow multiple communications. It controls the medium access operations using a time-frequency schedule. However, TSCH (Time Slotted Channel Hopping) specification does not specify how to build an optimized schedule. In this paper, we propose a distributed channel hopping scheme by providing an analytical model for the exploitation of Latin rectangles to avoid interference and collisions. Indeed, Latin rectangles are used to perform the scheduling process, where rows present the channel offsets and columns for slot offsets. Thus, the frequency of communication is derived using Latin rectangles, which prevents the scheduling function of nodes from considering channels already allocated in their neighbourhood. Consequently, interference and multi-path fading are mitigated with more reliability and robustness. Markov chain model for the queue on every node is introduced and takes the bulk arrivals and the slot distribution into account. We analyze the efficiency of this algorithm by analytical techniques and extensive simulations for three bulk arrivals: Poisson, Bernoulli, and Geometric.
... However, TSCH does not provide a specific way to create or manage communication schedules on its own. This means that additional design is required to optimize the utilization of network resources [3]. In a TSCH network, scheduling plays a key role in preventing data collisions and maintaining network performance. ...
6TiSCH networks adopt the IEEE 802.15.4e-based TSCH protocol to support efficient and reliable communication in low-power and lossy network (LLN) environments. However, under bursty traffic conditions, the traditional minimal scheduling function (MSF)-based scheduling technique cannot effectively handle the traffic load and suffers from packet queue overflow. In this study, we propose two main techniques to solve these problems. The first technique, dynamic cell cycle adjustment, dynamically adjusts the cell addition and deletion cycles based on the link quality and packet queue utilization to prevent packet queue overflow and efficiently use limited cell resources. The second technique, the parent node 6P transaction forwarding technique, is designed to pre-forward cell addition requests to higher nodes along the path when the cell utilization exceeds a set threshold due to traffic spikes at the lower nodes, so that the higher nodes can perform 6P negotiation immediately without waiting for MAX_NUMCELLS cycles. This minimizes the cell addition delay and prevents packet queue overflow. The simulation results show that the proposed technique has a high packet delivery ratio (PDR), low latency, and energy efficiency compared to conventional MSF, IMSF, and LMSF in various traffic environments. In particular, it maintains stable performance while preventing packet overflow under bursty traffic conditions. This work contributes to the optimization of scheduling and cell negotiation in 6TiSCH networks to improve the network efficiency and reliability in IoT environments.
... Dentre diversas abordagens com foco na melhoria das RSSFs, um destaque de grande importânciaé o Time-Slotted Channel Hopping (TSCH) [3] queé amplamente utilizado em diversos contextos, como sistemas de monitoramento industrial [4] e na integração de sistemas dedicados a ambientes agrícolas [5]. O TSCH propõe uma organização das transmissoões em células baseadas em tempo e canais de comunicação, agrupados em uma estrutura chamada slotframe. ...
Este estudo propõe o protocolo Dual-Radio-Dual-Slotframe (DRDS), uma nova abordagem para melhorar o desempenho de redes de sensores sem fio com rádio duplo. Baseado no Time Slotted Channel Hopping (TSCH) na camada MAC, o protocolo utiliza uma alocação combinada de slotframes para dois rádios. Implementado e avaliado no Contiki-NG e Cooja, o DRDS demonstrou superioridade em termos de vazão de pacotes em todos os cenários analisados, destacando-se como uma solução promissora para otimizar redes IoT.
... In Palattella et al. (2013), it was shown that TSCH protocol has superior energy efficiency compared with traditional IEEE802.15.4/ZigBee approaches. Standards of TSCH application can be found in Watteyne et al. (2015). ...
This paper considers the problem of sensory data scheduling of multiple processes. There are n independent linear time-invariant processes and a remote estimator monitoring all the processes. Each process is measured by a sensor, which sends its local state estimate to the remote estimator. The sizes of the packets are different due to different dimensions of each process, and thus it may take different lengths of time steps for the sensors to send their data. Because of bandwidth limitation, only a portion of all the sensors are allowed to transmit. Our goal is to minimize the average of estimation error covariance of the whole system at the remote estimator. The problem is formulated as a Markov decision process (MDP) with average cost over an infinite time horizon. We prove the existence of a deterministic and stationary policy for the problem. We also find that the optimal policy has a consistent behavior and threshold type structure. A numerical example is provided to illustrate our main results.
... In [18], the IETF's 6TiSCH working group incorporated TSCH [19] into the IEEE 802.15.4 standard to mitigate the impact of external interference by leveraging its channel hopping capability. Fig. 1b illustrates the 6TiSCH protocol stack. ...
In the industrial sector, we are currently witnessing the advent of the fourth industrial revolution, commonly known as the Industrial Internet of Things (IIoT). This cutting-edge digital technology relies heavily on connectivity, real-time processing, machine-to-machine communication, and machine learning. In this context, the imperatives of reliability, flexibility, mobility, and cost-effectiveness have led to a strong push towards Low-power and Lossy Networks (LLN) solutions for industrial systems. IP multicast presents a solution for a multitude of IIoT applications, ranging from over-the-air programming and information sharing to device configuration and resource discovery. It achieves this by enabling the transmission of packets from a single source to multiple destinations, thereby conserving resources such as bandwidth, energy, and time. The IPv6 over IEEE 802.15.4 TSCH (6TiSCH), as implemented, does not handle multicast traffic. To meet their needs, this document aims to propose an approach for implementing multicast in 6TiSCH networks via unicast without affecting MAC layer protocols. A series of extensive simulations were conducted to evaluate the performance of the multicast protocols under scrutiny. The obtained results not only affirm the viability of our proposed approach for implementing multicasting in 6TiSCH networks but also highlight the superior performance of ESMRF and BMFA when compared to MPL in terms of resource consumption and end-to-end delays. They also demonstrate the reliability of MPL.
... In this setup, time is segmented into slots of uniform duration that make up a slotframe. This slotframe is cyclically repeated, with each slot providing sufficient time for a pair of nodes to complete a single packet exchange and a following acknowledgment if needed [12]. The 6TiSCH architecture applies in low-power IoT scenarios, including smart industrial systems, buildings, infrastructure, and domestic setups. ...
The 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) architecture for wireless sensor networks merges the time-slotted channel hopping (TSCH) at the medium access control (MAC) layer with the routing protocols tailored for low-power and lossy networks (RPL). However, research often neglects the incorporation between TSCH MAC and RPL. Standard RPL strategies rely on an objective function (OF) using the expected transmission count (ETX) metric, which does not adequately reflect the traffic dynamics. Moreover, RPL’s hysteresis function employs a static threshold to control parent change decisions. This static setting disregarded the diverse traffic patterns within the network, leading to unnecessary parent node changes and preventing the node from selecting a better parent. To overcome these shortcomings, we introduce 3 advancements to standard RPL. First, an adaptive parent-changing mechanism based on cooperative Q-learning. Second, a cell usage and traffic load aware objective function. Third, an improved initial transmission cell allocation. Those methods are collectively termed ACI-RPL. We evaluated the performance of the proposed method through simulations using the 6TiSCH simulator and real-hardware tests on the FIT IoT-Lab testbed with OpenWSN firmware. The experiment result indicates that ACI-RPL performs better than the benchmark algorithms. In comparison to the standard RPL, ACI-RPL improves the packet delivery ratio and the total received packets by 12% and 17%, respectively. Additionally, ACI-RPL reduces energy consumption and latency by 23% and 9%.
... An ED recalibrates its clock with the network's global clock once it receives an ACK which contains information about the time passed since the beginning of the time frame. This is a usual concept that is applied in similar synchronized protocols [15]. The clock correction is a float number so 4 additional bytes are required in the ACK (16 bytes in total). ...
The Aloha-based channel access of LoRa-enabled devices is a challenging task due to the high potential for significant packet collisions. This paper proposes a Reinforcement Learning (RL) approach, wherein each end-device (ED) autonomously learns how to transmit data in time slots within a fixed time frame in order to alleviate collisions. The proposed approach offers an autonomous lightweight scheduling method eliminating the gateway's computational requirements for calculating comprehensive schedules. Comparative simulations conducted using the ns-3 network simulator against the Pure and Slotted Aloha approaches demonstrate significant improvements in packet delivery ratio. The results indicate that in a network with 300 EDs and a time frame of 200 seconds, RL approach achieves a delivery ratio of over 95%, showcasing a notable improvement of around 20 percentage points compared to Pure Aloha and 17 percentage points compared to Slotted Aloha.
... TSCH is a globally synchronized network where traffic is transmitted based on a static cyclic schedule table called slotframe C that repeats with the period equal to the slotframe size |C| [22], [23]. In a TSCH network, the slotframe (schedule) is divided into equal-length timeslots, as shown in Fig. 1. ...
The Internet of Things is shaping the next generation of cyber–physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a reinforcement learning (RL) framework to optimize the slotframe size of the time slotted channel hopping protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user's requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.
... TSCH is a globally synchronized network where traffic is transmitted based on a static cyclic schedule table called slotframe C that repeats with the period equal to the slotframe size |C| [22], [23]. In a TSCH network, the slotframe (schedule) is divided into equal-length timeslots as shown in Fig. 1. ...
The Internet of Things (IoT) is shaping the next generation of cyber-physical systems to improve the future industry for smart cities. It has created novel and essential applications that require specific network performance to enhance the quality of services. Since network performance requirements are application-oriented, it is of paramount importance to provide tailored solutions that seamlessly manage the network resources and orchestrate the network to satisfy user requirements. In this article, we propose ELISE, a Reinforcement Learning (RL) framework to optimize the slotframe size of the Time Slotted Channel Hopping (TSCH) protocol in IIoT networks while considering the user requirements. We primarily address the problem of designing a framework that self-adapts to the optimal slotframe length that best suits the user’s requirements. The framework takes care of all functionalities involved in the correct functioning of the network, while the RL agent instructs the framework with a set of actions to determine the optimal slotframe size each time the user requirements change. We evaluate the performance of ELISE through extensive analysis based on simulations and experimental evaluations on a testbed to demonstrate the efficiency of the proposed approach in adapting network resources at runtime to satisfy user requirements.
... It implies, that each object in the communication link should be trusted without authentication, as well as key management, time-synchronised communications, and reply protection. To address the lack of reply protection as well as time-synchronised communication, the IEEE 802.15.4e extension (modification) was introduced in 2012 by the IETF [35]. It is critical to understand that link layer security cannot safeguard packets once they leave its network. ...
Internet of Things (IoT) faces security concerns different from existing challenges in conventional information systems connected through the Internet because of their limited resources and heterogeneous network setups. This work proposes a novel framework for securing IoT objects, the key objective of which is to assign different Security Level Certificates (SLC) for IoT objects according to their hardware capabilities and protection measures implemented. Objects with SLCs, therefore, will be able to communicate with each other or with the Internet in a secure manner. The proposed framework is composed of five phases, namely: classification, mitigation guidelines, SLC assignment, communication plan, and legacy integration. The groundwork relies on the identification of a set of security attributes, termed security goals. By performing an analysis on common IoT attacks, we identify which of these security goals are violated for specific types of IoT. The feasibility and application of the proposed framework is illustrated at each phase using the smart home as a case study. We also provide qualitative arguments to demonstrate how the deployment of our framework solves IoT specific security challenges.
... TSCH-based scheduling's improved reliability is due mainly to channel speed, which has been termed the "HEART" of industrial lowpower wireless schemes such as Wireless-HART [2], ISA100.11a [3], and IETF-6TiSCH [4]. ...
This paper describes a simple and reliable deep
learning-based deep neural network (DNN) model that can
conduct time-slotted channel hopping (TSCH) based scheduling
in accordance with IEEE 802.15.4e guidelines. In a centralized
fashion, the TSCH network develops as a maximum weighted
bipartite matching strategy for links to cell assignment of a slot
frame. The cell assignment problem is solved using a wellknown Hungarian assignment algorithm, which considers
network throughput as a bipartite-edge-weight. We use the
Hungarian scheduling technique to create training data and
train the DNN accordingly. The results of the simulations show
that the proposed DNN-based scheduling scheme outperforms
Hungarian algorithm-based methods while using fewer
computational resources.
... In the IIoT, wireless low-power communication technologies became popular with the support of IEEE 802.15.4 standard. It uses a large number of nodes to establish an infrastructure that connects the number of devices to the internet by using low-power short-range wireless links [1][2][3]. To create optimal communication between smart industrial devices in IPv6 networks, the Institute of Electrical and Electronics Engineers (IEEE) and the Internet Engineering Task Force (IETF) standardization Working Groups (WG) are proposing several protocols. ...
Wireless low-power communication technologies play a vital role in building Industrial Internet of Things (IIoT) systems. The 6TiSCH layer is being developed for IIoT applications to use IPv6 upper stack over IEEE 802.15.4e TSCH MAC. 6TiSCH layer defines the scheduling scheme to achieve stringent industrial requirements and QoS advancement. This paper proposes a novel 6TiSCH traffic-aware autonomous scheduling technique for the IIoT network. In this technique, the schedule is created as per the traffic condition at the node, reducing the packet loss. It divides the slotframe into a number of segments and allocates a consecutive segment for the node as per its hop distance from the root. Supplementary cells are allocated to the nodes that are close to the root as these nodes have more traffic than the other nodes in the network. The proposed technique outperforms by improving the Packet Delivery Ratio (PDR) and reducing end-to-end delay compared to other scheduling techniques. The result shows that PDR is improved up to 23% and reduces delay up to 37%.
... In such a case, the nodes can achieve a higher bit rate. However, more power will also be consumed [39]. TSCH enables the nodes to schedule their transmissions on specific channels using a frequency hopping pattern. ...
Non-Orthogonal Multiple Access (NOMA) is one of the promising technologies for wireless communication networks. Although NOMA was originally proposed in cellular networks, due to its strengths, it can also be used in other networks, such as wireless sensor networks (WSN). Massive connections and energy limitations are some of the challenges in WSNs and NOMA can be used to improve spectral efficiency, reduce latency, and increase energy efficiency of these networks. In this paper, we investigate the effect of Power-Domain NOMA (PD-NOMA) on the performance of IEEE 802.15.4e Time Slotted Channel Hopping (TSCH)-Based WSNs. A clustered WSN is studied in which sensor nodes send their data to their cluster heads using NOMA transmissions, and where the cluster heads will also utilize NOMA for the transmission of their aggregated data to the sink node. A fair user grouping and power allocation scheme is proposed in PD-NOMA where the users utilizing the same channel and their corresponding power levels are determined. A new clustering algorithm is also proposed to select the appropriate cluster heads for the NOMA transmission. Simulation results show that the proposed scheme improves energy efficiency, latency, and network throughput in IEEE 802.15.4e TSCH-based WSNs.
... In the TSCH mode, the slot-frame size is limited between 10 to 1000 time-slots. The slot-frame's size depends on the application needs [20,25]. ...
The IEEE 802.15.4e specified the Time Slotted Channel Hopping (TSCH) that uses multi-channels and shared links to ensure a reliable and efficient data transmission in IoT applications. However, the standard does not define any scheduling mechanism for the network configuration. The main problem in TSCH is triggered when hidden nodes in a shared link transmit data at the same time. A collision happens even if the hidden nodes apply CSMA/CA before starting data transmission. To solve this problem, we propose Interference Collision Free Scheduling (ICFS), and Interference Collision Free Scheduling-Without Redundant Data (ICFS-WRD) algorithms to reduce the internal collisions caused by hidden nodes on shared links. The ICFS-WRD approach stands in contrast to proposals in the recent literature where shared links of the proposed TSCH schedules are free from colliding nodes. ICFS-WRD intentionally schedules the colliding nodes that sense redundant data on the same shared link, and let them alternate in transmitting data. This mechanism is targeted to sparse more cells for future flows, reduce the slot-frame size, increase the network’s lifetime and avoid transmitting redundant data. We propose a clustering technique to build a multi-hop cluster based convergecast traffic routing approach with a unique sink, on which we implement the scheduling algorithms. The proposed algorithms have been tested through simulations using network simulator NS3. The results show improvements in terms of energy consumption (ICFS saves approximately 23%), packets delivery ratio (ICFS achieves approximately 96.5%), and latency (ICFS-WRD delivers the packets twice faster than ICFS). We discuss some theorems and proofs that show that ICFS-WRD reduces the slot-frame size, increases the network lifetime, avoids transmitting redundant data, and minimizes the network’s congestion.
... Frequency Hopping in wireless technologies. Multiple wireless technologies feature frequency hopping to mitigate interference, such as IEEE 802.15.4e-TSCH [18]- [20], Bluetooth Classic [21]- [23], 5G [24], and to access unlicensed bands [25]. In TSCH, A-TSCH measures and excludes channels with high background noise via RSSI measurements [19]; Elsts et al. combine RSSI measurements and the Packet Reception Rate (PRR) to detect channels with low performance [20]. ...
With more than 4 billion devices produced in 2020, Bluetooth and Bluetooth Low Energy (BLE) have become the backbone of the Internet of Things. Bluetooth and BLE mitigate interference in the crowded 2.4 GHz band via Adaptive Frequency Hopping (AFH), spreading communication over the entire spectrum, and further allows the exclusion of interfered channels. However, exclusion is challenging in dynamic environments or with user mobility: as a user moves around, interference affects new channels, forcing AFH to deprive itself of new frequencies, while some other excluded channels are now free of losses but remain excluded. Channel re-inclusion is a primordial, yet often left out, aspect of AFH, as it is non-trivial to assess the new situation of excluded frequencies. We present eAFH, a mechanism for channel exclusion and inclusion. eAFH introduces informed exploration to AFH: using only past measurements, eAFH assesses which frequencies we are most likely to benefit from re-including in the hopping sequence. As a result, eAFH adapts in dynamic scenarios where interference varies over time. We show that eAFH achieves 98-99.5% link-layer reliability in the presence of dynamic Wifi interference with 1% control overhead and 40% higher channel diversity than state-of-the-art approaches.
... For instance, if the nodes operate in the 2.4GHz frequency band, it takes approximately 4 ms to transmit the maximum size MAC frame and extra 1 ms for the ACK. If the time slot is set to 10 ms, then 5 ms is used for radio turnaround, packet processing, and security operations [11]. ...
Recent applications in large-scale wireless mesh networks (WSN), e.g., Advanced Metering Infrastructure (AMI) scenarios, expect to support an extended number of nodes with higher throughput, which cannot be sufficiently supported by the current WSN protocols. Two prior protocols, Wi-SUN Field Area Network (Wi-SUN FAN) and IETF 6TiSCH standards, are popularly used that are respectively based on asynchronous Carrier Sense Multiple Access / Collision Avoidance (CSMA/CA) and IEEE 802.15.4 Time Scheduled Channel Hopping (TSCH). However, the former one with CSMA/CA can be prone to the Hidden Node Problem (HNP) that leads to a degradation of reliability, while the latter one with TSCH avoids HNP by using synchronously scheduling but requires massive control signalling that degrades the upper-bound of throughput. Accordingly, this paper tackles the challenge of how to improve the upper-bound of throughput without loss of reliability. To do so, we first present an in-depth evaluation of reliability and throughput between the two existing standards via system-level simulation. We then propose a self-configurable grouping (SCG) method to cluster nodes without using location information of each node. This SCG method eliminates around 99\% HNP in CSMA/CA, thus greatly improves its reliability with maintaining the relatively high upper-bound of throughput. Our results show that the SCG method almost doubles the network throughput compared with both 6TiSCH and Wi-SUN FAN in heavy traffic scenarios, while providing extremely high reliability of more than 99.999\%.
... We use the unslotted CSMA/CA mechanism in an IEEE 802.15.4 network in non-beacon mode in our experiments. Timeslotbased alternatives, such as 6TiSCH [63] and ISA100.11a [64], can be used to improve the reliability and throughput of IoT sensing systems at the cost of increased complexity in time synchronization [65], [66]. ...
A sink is a node in a sensor network that functions as a gateway, and it gathers data from other nodes and sends the data over the Internet for further processing. However, a single sink cannot meet the demands of a sensor network for the Internet of Things (IoT) with heavy traffic. Although deploying multiple sinks can improve scalability and mitigate bottleneck problems, it is still challenging to use multiple sinks while keeping energy consumption down. Previous studies have addressed this issue using optimization techniques based on the assumption that the network converges to a steady state in terms of traffic load. We take a practical approach based on real-time changes in traffic load. This work introduces the concept of dynamic sinks, a sensor device that can serve as an on-demand sink. We identify suitable metrics for decision mechanisms to activate/deactivate dynamic sinks and investigate three decision schemes, namely autonomous, delegated, and centralized schemes. We also develop a protocol to disseminate the decisions. As a proof-of-concept, the dynamic sink protocol is implemented in Contiki. We evaluate trade-offs between packet delivery ratio (PDR) and energy consumption using emulated devices in the Cooja network simulator. The results show that setups with dynamic sinks can reduce energy consumption considerably at the expense of slightly lower PDR when compared to a setup with multiple permanent sinks.
A wireless sensor network (WSN) is a distributed sensor network of wireless devices that can gather and communicate information through wireless links. The gathered information will be sent to the base station or sink for further processing. The Sensor in WSN communicates wirelessly, so device location can be changed at any time and network settings should be flexible. WSN are spatially distributed autonomous sensors to monitor physical environmental conditions such as temperature, sound, pressure etc. It started to connect with internet of things (IoT) through internet which has also an ability to connect the sensor nodes. Now, huge amounts of data which are collected by WSNs are being accessed by IoT. The objective of this paper is to identify security challenges and various issues in wireless sensor networks which are integrated with IoT. The wireless sensor network currently must deal with the main issues that are security. For certain type of attacks like eavesdropping, jamming, and spoofing. Finally, to address security and privacy issues in IoT domain of WSN a light has been shed on a few obstacles and potential future research avenues by using Machine Learning algorithms.
Industrial low-power wireless networks exhibit unique requirements in terms of reliability, battery lifetime and security. Time Synchronized Channel Hopping is a networking technique created to address these needs, which was standardized by working group IETF 6TiSCH. Analog Devices’ SmartMesh product lines have been the best-in-class TSCH implementation, exhibiting over 99.999% wire-like end-to-end reliability, a decade of battery lifetime, and certified security. With over 100,000 networks deployed, SmartMesh plays a market-leading role. Today, SmartMesh runs on the LTC5800, and often requires customers to drive it from a second external micro-controller, which increases cost. This paper introduces a port of SmartMesh to the MAX32655, a dual-core microcontroller and radio System on Chip: the RISC-V core runs the communication stack, the ARM Cortex-M4 core is left available to the customer. We show how that ARM Cortex-M4 core can run a MicroPython interpreter for faster prototyping and time-to-market. A simple script, designed to transmit packets over a SmartMesh network using MicroPython, requires only an additional 6 kB of RAM. Furthermore, by compiling this script into byte code, the processing time can be reduced to a quarter of its original duration.
LoRa is widely used in low-power Internet of Things (IoT) applications due to its low power consumption, high connection density, and wide coverage. How to optimize the physical layer parameter configuration of LoRa edge nodes is a bottleneck issue that restricts network performance. To reduce the energy consumption of the distributed LoRa networks, a reinforcement learning strategy based on link prior knowledge is adopted, and the resource allocation of the edge node of the LoRa networks is transformed into a multi-armed bandit problem. On this basis, a dynamic parameter selection algorithm suitable for LoRa edge nodes is proposed, namely the Link-Weight-EXP3 (LI-WEX) algorithm. By defining the weight factors of parameters and rewards for nodes, the energy consumption factor in decision-making is enhanced, and the policy space at the node is processed based on link knowledge, thereby improving energy efficiency by selecting the optimal combination of parameters. The simulation results show that the LI-WEX algorithm not only converges faster and is robust in different scenarios, but also can effectively reduce network energy consumption.
The growth of the Industrial Internet of Things (IIoT) has highlighted the need for reliable communication systems that ensure high network reliability, low power consumption, and deterministic latency. Therefore, an effective routing strategy is needed in such networks to achieve effective communication. While the 6TiSCH working group introduced the RPL (IPv6 Routing Protocol for Low power and Lossy Networks), it has limitations in challenging industrial environments where diverse wireless technologies may degrade link quality. As RPL's objective functions do not consider actual link quality, nodes might select parents and paths with suboptimal link quality. This paper addresses this issue by incorporating link quality in parent selection and proposing an enhanced RPL for 6TiSCH Networks. Through trace-based simulations, the proposed techniques showcase superior performance in achieving high reliability and low latency when compared to RPL.
The emergence of IPv6 (Internet Protocol Version 6) for low-power wireless communication is considered a breakthrough allowing a densely populated multi-hop network of Internet of Things (IoT) devices to be used for data gathering over a range of 1-2 kilometer (km). However, the communication between the devices has suffered from external interferences and multi-path fading challenge. The Internet Engineering Task Force (IETF) and Institute of Electrical and Electronics Engineers jointly proposed The IPv6 over IEEE 802.15.4 TSCH mode (6TiSCH) to deal with existing challenges and improve network performance to meet key requirements of industrial applications. The 6Top layer integrates TSCH (Time Slotted Channel Hopping)-MAC over IEEE 802.15.4 with the rest of the IPv6 stack where the schedule allocation is performed by scheduling function (SF). However, network scalability remains an open challenge. Specifically, the 6TiSCH Working Group (WG) do not define rules towards optimal schedule allocation over Time Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4. In this paper, we propose Decentralized, and Broadcast-based Scalable Scheduling Reservation Protocol for 6TiSCH Networks (DeSSR). The experimental performance analysis demonstrates strong performance under steady and bursty traffic when compared with current SFs. This makes DeSSR a strong proposal contributing towards improving scalability in large-scale 6TiSCH networks.
Current technology on the use of fifth generation (5 G) networks relies on IPv6 routing protocol (RPL) for low-power and lossy networks (LLNs). However, the constrained-resource nature of Internet of things (IoT) devices for LLNs makes RPL limited in routing functions and in need of enhancements in its objective functions (OFs) when selecting preferred parents (PPs) among nodes for optimized routing decisions while satisfying varied IoT applications requirements. We explore the vast application areas of LLNs and advances made in supporting operating system platforms as well as RPL enhancements. We observed that recent studies focus more on routing optimization for PPs selection in LLNs and node density management under varying traffic load, targeting a diversity of IoT applications requirement. Strengths and weaknesses in metrics adopted by literature are presented with suggestions to overcoming identified challenges. Evidently, the lack of real-time data has greatly declined ground-truth verification of RPL metric(s), demanding intelligent techniques for improved performance and meaningful connectivity scale up. This work proposed an integrated machine learning (ML) framework for RPL functionalities enhancement in IoT-based networks. Findings from the review revealed that using ML techniques could facilitate the deployment of several desired parameters for significant LLNs performance improvements.
There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes, which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this article, we present a novel trust-model-driven system architecture , based on behavioral evidence , that is suitable for resource-constrained IoT devices and supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviors. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behavior. Hence, we perform a threat modeling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication, and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used.
This chapter defines the IoT protocol stack and compares it to the existing Internet Protocol stack. It provides a layer-by-layer walkthrough of that stack and, for each such layer, discusses the challenges brought forward by the IoT requirements of the previous chapter, the industry progress made to address those challenges, and the remaining gaps that require future work. Starting with the link layer, the chapter discusses the impact of constrained device characteristics, deterministic traffic characteristics, wireless access characteristics, and massive scalability on this layer. It then covers the industry response to these challenges under various standards by IoT layers.KeywordsIoT protocolsIEEE 802.15.4TCSHIEEE 802.11ahTSNLLNs6LowPANRPL and 6TiSCHCoAPMQTTAMQPLPWANLoRaWAN
In this chapter, we develop a mathematical model for alcohol-related health risks incorporating fuzziness in uncertainties associated with individual risk behavior and induced death rate. In the study, fuzzy numbers (sets) are defined as the degree of peer influence of susceptible individuals into drinking. Using the next generation matrix operator (NGM), we derive the fuzzy reproduction number and characterize the existence of equilibrium states and their stability properties. The study reveals that perceived most influential individuals tend to increase the force of influence. The model has the potential to reveal inherent risk behaviors or cultural beliefs which are critical in the development of alcoholism management strategies.KeywordsFuzzy modelsHealth risksAlcoholismCultural beliefsFuzzy risk reproduction number
Internet of Things (IoT) is an interconnected wireless network where smart nodes (IoT devices) interact with each other in order to exchange data through the communicating medium. IoT technology has become important for people to build smart systems upon the use of technology. Internet of things is realized by the idea of free flow of information among various low-power embedded devices that use the Internet to communicate with one another. In the recent past IoT have grown rapidly and have become an extension of existing universal Internet. It can be easily anticipated that large scale systems equipped with numerous sensors will prevail in our society. With the rise of the Internet of Things (IoT) technology, the number of IoT devices/sensors has also increased significantly Billions of smart devices connected to IoT environment can communicate among themselves using sensors and actuators. This rapid growth and inclusion of IoT technologies in our daily life are facing challenges since most of the devices specially sensors in IoT network are resource constraint in terms of energy, computation capability, etc. Data collected from these sensors sent through the middleware like gateway, routers, etc. to the cloud servers or toward various analytical engine for meaningful knowledge discovery. These processed data and knowledge have lately attracted huge attention, and organizations are excited about the business value of the data that will be generated by deploying such networks. With this advent of IoT it has also attracted various security and privacy concerns. Due to the structurally open IoT architecture and the tremendous usage of the paradigm itself, causes to generate many unconventional security issues for the existing networking technologies. Moreover, since sensor nodes are cooperative within the IoT network, this sharing of data can create new challenges that can disrupt the systems’ regular functionalities and operations. In another aspect the growth of IoT technologies has enhanced by assimilating them with cloud computing and the era IoT-cloud has emerged. With these, some new class of security and privacy issues have also introduced. Furthermore, the commercialization of the IoT has led to several public security concerns including threats of cyber-attacks, privacy issues, and fraud crimes. This chapter gathers the needed information to give a complete picture of security issues and problems faced in IoT communication. In this chapter, we detail the major security as well as privacy issues more specifically. An extensive description of security threats and challenges across the different layers of the architecture of IoT systems is represented. The issues related to IoT-cloud is also highlighted. The light will be shed on the state-of-the-art solutions to the emerging and latest security issues in this field. We hereby present the evolving resolve policies as mined from the research work of various authors in this field that will expose the several research areas in IoT-cloud era. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Internet of Things is emerging as a commercial phenomenon. It is conjectured that billions of appliances will be connected within the current Internet infrastructure by 2020. These networked appliances require low-power radio and reliability. Hence, IEEE brought up a very efficient, reliable and deterministic time-frequency enabled medium access control protocol, IEEE 802.15.4e time slotted channel hopping (TSCH), on top of low power IEEE 802.15.4 radio for the industrial market. IEEE 802.15.4e TSCH is able to run a communication schedule for MAC upon a communication schedule is built. However, the issue of how such scheduling will be made, updated, and maintained is beyond the scope of the IEEE 802.15.4e TSCH standard. The unit responsible for these tasks is also out of its scope. That means, required scheduling mechanism is absent in this standard. In order to complete this gap, several communication scheduling algorithms are introduced in the literature. In this study, we first introduce another decentralized divergecast communication scheduling algorithm, called as DIGEST, where divergecast implies that network traffic moves in any directions opposed to convergecast where network traffic moves from leave nodes to root. That DIGEST algorithm tries to schedule time slots by selecting a neighbor node at first. When choosing a neighbor node, it tries to make almost equal number of dedicated time slots to each neighbor. In this way, it can provide better schedules with neighbor nodes than other scheduling algorithms. We evaluate the effectiveness of DIGEST, Diva, and Berkeley’s Aloha-based scheduling algorithms under static and mobile environments by running extensive simulations. Our simulation results indicate that the DIGEST algorithm is superior to the others in terms of the neighbor connectivity ratio.
The idea of the Internet of Things (IoT) has emerged in recent years and is growing rapidly. The main aim is to connect real‐world things over the cloud to the Internet. The various real‐time applications such as farming, weather forecasting uses rain, temperature, moisture, and loam sensors which are connected to an Internet. Thus, various kinds of information such as temperature, moisture, humidity, and rain can be processed later by using data analytics method to identify these and make effective decisions and approaches for monitoring smart environment. Similarly, traders of online shopping uses online data collected through various online shopping clouds through servers to analyze which product is to be more in demand, etc. The reflective transformation in economical system shall be possible in coming years through cloud‐based data analytics which can be said as reliable, sustainable, and robust system. The transformation of modernization can only be achieved with persistent usage of information technologies and communication technologies to manage as well as integrate this complete system. Therefore, by providing parallel processing of data and distributed data storage, cloud computing has been envisaged as an emerging technology of making possible this integration. Due to rapid up‐gradations in IoT, the Industry 4.0 grows and to handle the issues of large amount of data storage and its processing, cloud came up with variations of data storage and management strategies. Along with support of combination of cloud and IoT, the working styles in many fields become easier. The various challenges and issues have discussed in this chapter. The communication technologies that vary according to the application requirements have been depicted in this chapter. The today's working scenario and life style have been conquered by combination of IoT and cloud. It has been reached to every little part of the human life right from the monitoring of health, farming, smart industries, smart home, metering, video surveillance, etc. This chapter is designed for readers who intend to begin cloud‐based data analytics research with detailed knowledge and compile challenges, issues, organize, research avenues, and summarize using cloud. This chapter discusses the overview of the potential applications of cloud computing in smart working systems and case studies. It also describes the main technologies and innovations that will support the smart environment. The organization of the chapter is followed by subsections including introduction, challenges and issues, data models and applications, etc.
Common use cases in the Industrial Internet of Things (IIoT) deploy massive amounts of sensors and actuators that communicate with each other or to a remote cloud. While they form too large and too volatile networks to run on ultrareliable, time-synchronized low-latency channels, participants still require reliability and latency guaranties. We elaborate this for safety-critical use cases. This paper focuses on the effects of networking protocols for industrial communication services. It analyzes and compares the traditional Message Queuing Telemetry Transport for Sensor Networks (MQTT-SN) with the Constrained Application Protocol (CoAP) as a current IETF recommendation, and also with emerging Information-centric Networking (ICN) approaches, which are ready for deployment. Our findings indicate a rather diverse picture with a large dependence on deployment: Publish-subscribe protocols are more versatile, whereas ICN protocols are more robust in multihop environments. MQTT-SN competitively claims resources on congested links, while CoAP politely coexists on the price of its performance.
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