The rapid expansion of the Internet of Things (IoT) demands effective communication protocols that accommodate mobile and static end devices (EDs). LoRaWAN (Long Range Wide Area Network), a pioneering low-power wide-area network (LPWAN) technology, uses Adaptive Data Rate (ADR) approaches to optimize resource allocation, particularly for static EDs. However, traditional ADR approaches are ineffective in mobile contexts as they struggle to adapt to changing network conditions, resulting in significant packet loss and higher retransmission rates. Although innovative technologies, such as the Blind ADR (BADR), have been devised to improve the performance of mobile EDs, they still fall short of dealing with the unpredictable nature of mobile EDs. To address these challenges, this paper presents a novel HybridQ-ADR mechanism suitable for static and mobile EDs. This approach addresses the constraints of BADR and related methods in mobile scenarios. In particular, it provides a more efficient solution to reduce packet loss and collisions in dense and dynamic LoRa-based IoT networks. This is achieved by allocating Spreading Factors (SF) using signal orthogonality to minimize interference and provide reliable communication. Furthermore, the proposed HybridQ-ADR mechanism provides a new clustering technique based on estimated path loss. Specifically, it divides EDs into clusters and assigns different channels to each cluster, improving SF allocation and data transmission speeds. The proposed HybridQ-ADR mechanism includes a mobility-aware, Doppler-constrained SF allocation strategy, limiting each ED’s maximum SF based on its Doppler/mobility load to maintain reliable performance at high speeds. Performance evaluations using simulations and testbed implementations show that HybridQ-ADR improves latency, packet success rate, power consumption, and throughput for both static and mobile EDs.

As the Metaverse evolves, robust authentication is essential to protect digital avatar privacy from identity threats such as theft, unauthorized access, and avatar spoofing. A user’s gait, serving as an intrinsic biometric signature of their avatar, offers a seamless and continuous authentication mechanism, enhancing security. Traditional authentication methods, including passwords, biometrics, and facial or fingerprint recognition, face challenges in virtual environments due to occlusions, spoofing risks, and hardware dependencies. To address these limitations, we introduce AutoGaitAnalyzer, a novel gait authentication framework that uses 16 gait features from a large-scale simulation of 5,000 users. Benchmarked against over 10 state-of-the-art models, AutoGaitAnalyzer outperforms all, establishing a new standard for avatar security in the Metaverse.

Low-power wide-area networks (LPWAN) have substantially improved the Internet of Things (IoT). LoRaWAN is a potential technology for IoT applications because it uses low-power, long-distance communication and offers excellent availability with low energy consumption. LoRaWAN power consumption can be reduced using the pure Aloha protocol at the MAC level. Optimizing orthogonal transmission parameters is still a major difficulty for enhancing network performance, even though they reduce packet loss and prevent collisions, especially in dynamic and heterogeneous networks. However, the challenge of random channel selection in LoRaWAN communication often leads to inefficient resource utilization and degraded network performance. This paper proposes a novel game-theoretic approach for optimal channel selection in LoRaWAN networks. Our method leverages real-time Received Signal Strength Indicator (RSSI) data and a non-cooperative game theory model to dynamically select channels, thereby improving throughput and reducing packet loss. Through extensive simulations and a realworld testbed, we demonstrate that our proposed mechanism outperforms existing approaches such as the Online Decision algorithm and MFMSF. Specifically, it achieves up to$22 \backslash \%$improvement in throughput,$15 \backslash \%$higher packet delivery ratio, and$18 \backslash \%$reduction in latency, while consuming up to$25 \backslash \%$less energy under heavy and dynamic traffic conditions. This work offers a significant advancement in enhancing the scalability and reliability of LoRaWAN networks, paving the way for more efficient IoT communications.

The IEEE 802.15.4 standard is one of the widely adopted networking specifications for realizing different applications of the Internet of Things (IoT), One of its Medium Access Control (MAC) protocols, the Deterministic Synchronous Multi-channel Extension (DSME), enhances stringent QoS by allocating DSME-Guaranteed Time Slots (GTSs) between pairs of devices. However, the standard does not specify a mechanism for scheduling these DSME-GTSs, presenting numerous research opportunities in this area. In this paper, we propose a novel Dynamic Hierarchical Slot-Channel Allocation with Recursive Weighting (DyHSARW) scheme aimed at improving the scheduling of DSME-GTS in large-scale IEEE 802.15.4-based IoT networks. The proposed approach dynamically allocates non-overlapping time slots (using the HCF technique) across multiple channels (based on a device's association order), optimizing resource utilization while adapting to the hierarchical structure of the network. Specifically, the HCF condition checks if the transmission weights of a child-parent pair and the previous time slot's value are compatible, i.e., if their HCF is equal to 1. This condition indicates that the parameters are co-prime, which minimizes the likelihood of collision in the time slot allocation process. This method seeks to overcome the limitations of existing GTS scheduling algorithms, which face challenges in efficient resource allocation, increased latency, and higher energy consumption under dynamic traffic conditions.

For large-scale, remote, and harsh environments-based Industrial-IoT applications, the long-range wide-area network (LoRaWAN) is a low-power protocol that enables reliable, robust, and long-range communication. A critical aspect of LoRaWAN is the adaptive data rate (ADR), which allows end devices in Industrial Internet of Things (IIoT) applications to modify the data rate dynamically according to channel conditions. This guarantees efficient connectivity and prolongs the battery life of an end device. Data loss in harsh IIoT situations can be huge because the channel traffic tends to vary, which might include significant congestion and interference. There are several issues with the present ADR method, including its slow convergence time and lack of adaptability. Furthermore, in large IIoT applications, the ADR approach is overwhelming in high-traffic networks since it only considers link-level performance and assigns configuration parameters like spreading factor (SF) and transmission power (TP) to individual end devices. As a result, network performance could be improved. This article proposes an effective cluster-based ADR mechanism (Cluster-ADR) to minimize collisions and packet loss. The proposed Cluster-ADR clusters the end devices based on the estimated path loss. To address the aforementioned issues, this article proposes an effective Cluster-ADR by employing signal orthogonality to allocate SFs to minimize collisions and packet loss in dense LoRa-based IIoT networks. A clustering method based on measured path loss is also presented that clusters end devices, assigns different channels to each cluster, and allocates optimal SFs for each end device within the cluster. Additionally, this article presents an analytical computation of the energy consumption and convergence time of standard and the proposed ADR mechanisms using a Markov model. The performance of Cluster-ADR is examined in terms of packet success rate, power consumption, and convergence time using simulations and testbed implementation.

Advancements in deep neural network accelerator architectures have resulted in the proliferation of Convolutional Neural Network (CNN) applications in computer vision. These energy-efficient accelerators provide considerable performance improvement and incur low area overhead. Due to restricted power and area constraints, this enables the accelerators to be ideally suited for several real-time mobile and edge device applications However, the major challenge is deploying the continuously growing, complex deep CNN s on embedded devices that are constrained in nature in terms of area and power. In addition, these applications generate an enormous amount of data, and accommodating them in an embedded device is also challenging. To alleviate the computational complexity of CNNs on embedded devices, this work proposed an algorithm that introduces a design trade-off between accurate computation and approximate computation. The approximate computing method reduces the computation workload and enhances the speed as well as the power efficiency of error-resilient applications. We present an approximate Vedic multiplier architecture optimized for area, power, and delay. An accelerator architecture has also been developed that computes the CNN inference model in multiple channels parallelly without any penalty on the network's accuracy or the hardware cost. It is shown in the experimental analysis that the proposed computing architecture enables the embedded devices to work with low power and area. The proposed accelerator achieves a 5.3 % improvement in computing throughput over the state-of-the-art accelerators.

The Long Range Wide Area Network, or LoRaWAN, is a low-power networking protocol mostly used with Adaptive Data Rate (ADR) for static end devices (EDs) to connect battery-operated EDs to the internet wirelessly. However, ADR suffers from power dissipation and suboptimal settings in dynamic network topologies and changing channel traffic conditions, causing prolonged convergence time. We aim to tackle this issue by introducing a novel channel traffic estimation metric, which, combined with ED path loss, helps achieve faster ADR convergence. Our method minimizes the effect of channel instability and maximizes data transmission efficiency by constantly modifying ADR parameters. Our approach decreases retransmissions and improves convergence time over existing ADR approaches in dynamic LoRaWAN networks, as shown by extensive simulations.

The prominence of the Internet of Things (IoT) has surged in recent years due to tech-nological advancements, increased connectivity, and the widespread adoption of smart devices. Currently, IoT solutions are pervasive across various sectors, including smart homes, wearable devices, health-care, transportation, and industrial automation. The Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol designed to provide reliable connection over long distances, spanning many kilometers, using low-power and wide-area networks (LPWANs) often used in IoT applications. However, LoRaWAN is vulnerable to bit-flipping attacks, where transmitted data is intercepted and maliciously altered by flipping specific bits in the message payload. Such attacks have the potential to undermine the trustworthiness and dependability of the system. In this paper, we propose a novel security enhancement for LoRaWAN. Our scheme incorporates a hash digest along with the end device's payload, enabling the application server to detect any malicious alterations in the received content using the hash value. We analyze the security of the proposed scheme against bit-flipping attacks and measure its performance through experiments in a real testbed. Our observations indicate that the proposed mecha-nism not only secures communication against these attacks but also incurs minimal overhead in terms of power consumption, transmission overhead, and latency.

Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol that facilitates efficient and wide-range communication under low-power conditions. Over-the-air activation (OTAA) is a process recommended by Lo-Ra Wan v1.0.4 (latest version) that enables end devices to join the network with the help of the joining server and generate the session keys for further communications. However, OTAA is vulnerable to potential security threats due to unencrypted join request messages and the reuse of the same encryption keys. In this paper, we present a new security enhancement that addresses the aforesaid security issues. In the proposed scheme, we encrypt the join request message using a secret key to ensure data confidentiality. In addition, we incorporate the use of a random nonce in the proposed joining procedure to protect LoRaWAN against attacks related to the reuse of the same key. We show that the adversary cannot learn sensitive information from the join request message and reuse the AppKey to execute the eavesdropping and unauthorized activation attacks with non-negligible probability.

As the Internet of Things (IoT) is poised to become a global phenomenon, it is imperative to schedule the transmissions of IoT devices effectively and in a fair way. Leveraging Long Range (LoRa) technology, we can achieve transmissions that consume minimal power while covering vast distances, aligning with the requirements of IoT devices. However, the proximity of multiple devices within the same area often leads to packet interference and collisions. To address this, our study introduces a pioneering scheduling method utilizing a constellation of Low Earth Orbit (LEO) satellites to manage and streamline the transmission of data from End Devices (EDs). This method employs two LEO satellites: the first satellite assigns the sequence for EDs to dispatch their packets, and the second collects these packets in the predetermined sequence before forwarding them to the LoRa Network Server (LNS). For urgent (URG) communications, EDs can alert the first satellite, which then coordinates with the LNS to schedule these priority transmissions. The LNS generates a schedule that is relayed to the second satellite, informing EDs with URG packets of their specific transmission times and channels. This scheduling approach is designed to optimize channel usage effectively while accommodating the transmission of urgent data.

LoRaWAN (Long Range Wide Area Network) is a low-power, wide-area wireless communication protocol designed specifically for the Internet of Things (IoT) and machine-to-machine (M2M) applications that enable long-range, bidirectional communication between low-power devices. Lo-RaWAN employs Adaptive Data Rate (ADR) technology to dynamically adjust the data rate for each device based on its signal quality and distance from the gateway. ADR enables improved network performance, extends device battery life, and simplifies network management, making LoRaWAN suitable for various IoT deployments. However, the end devices' inefficient utilization of radio resources (e.g., spreading factor and transmission power) significantly degrades network performance, device battery life, and adaptability to changing network conditions. Machine Learning (ML) algorithms analyze and optimize the real-time network conditions to enhance network performance. This work aims to develop an ML-based approach that adaptively selects the most suitable Spreading Factor (SF) for end devices (ED). Two independent ML algorithms such as K-means and Reinforcement Learning (RL) have been applied to EDs and Gateways, respectively, to dynamically allocate SF for both static and mobile EDs. Through simulations, the performance of the proposed mechanism is analyzed in terms of packet success rate, convergence time, energy consumption, latency, and throughput.

Fog computing is an extension of cloud computing and presents additional devices called fog devices near IoT devices providing services on behalf of cloud servers. Although fog computing brings several advantages, rapid growth in the data generated by IoT devices increases communication and computational costs. As data sensed by IoT devices may correlate, there is a high possibility of duplicate copies in the sensed data. Data deduplication is a compression technique that reduces communication and storage overhead by skipping the upload and storage of duplicate copies of data. However, deduplication for a fog-enabled IoT system introduces new security issues. In this paper, we propose a novel secure deduplication approach with dynamic key management in a fog-enabled IoT system. We introduce a multilayer encryption scheme with dynamic key management to prevent the access of revoked users to the data. We implement the proposed scheme in a realistic scenario using Raspberry Pi and Firebase cloud services. The performance analysis shows that our approach achieves confidentiality and forward secrecy along with lower storage, computational, and communication costs.

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads.

Enabling smart technologies in agriculture has led to the improvement of crop productivity. In India, agriculture is a primary occupation, and 70% of the population is dependent on it. Plant diseases cause significant losses in an agriculture-oriented economy. Timely monitoring of plant health and detecting plant disease is a laborious process. Automated monitoring and detection techniques hold great promise for identifying plant condition and providing useful information to facilitate effective agricultural management measures. Deep learning (DL) algorithms improve the detection accuracy in many computer vision applications of smart and precision agriculture. This paper presents a plant disease detection and classification method using YOLOv3 (You Only Look Once) model to design an Internet-of-Things (IoT) device. An approximate computing technique has been adopted that minimizes the computational complexity of DL algorithms to deploy on any embedded devices efficiently. The proposed model achieves an average of 96.92% of classification accuracy while detecting plant disease for three different classes.

The demand for a smart classroom has been compounded by Covid-19, which allows students to have a meaningful learning experience while staying home. Students who join a classroom in online mode don't have the opportunity to experience a classroom setting because of the hybrid mode of teaching (both online and offline classes). As a result, they have problems such as not being able to see the board clearly, not being able to follow the lecturer because he or she is out of frame, and thus having difficulty learning. Furthermore, this results in lower interaction between the online students and the professor. To teach effectively, the professor is unable to use the entire length of the board as it would not be visible to students joining in online mode. As students and instructors, we identified the issue and developed a plug-and-play device which is portable to address the aforesaid problem during this testing and difficult period of time of the pandemic. The paper outlines the practical implementation of a plug-and-play device that meets the aforementioned requirements. The model also considers power usage, as it can dynamically control energy-consuming resources such as lighting and air conditioning in response to the environment and the presence of students.

The emergence of applications in vehicles requires computational capability which poses a major challenge in mo-bile edge computing. In this paper, we have proposed the token-based predictive offloading scheme with the aim of providing the optimal cost of offloading and reducing the average delay. Specifically, the proposed scheme uses a token-based approach for offloading the data from vehicles to MEC(Mobile Edge Computing) server. We have designed a separate table for MEC servers to show the status of consumed tokens and available tokens and another table for vehicles to store the status for another vehicle for vehicle to vehicle (V 2 V) communication. Dedicated Short Range Service (DSRC) technology is used to facilitate the communication between vehicle to vehicle and vehicle to infrastructure(e.g.toll gate). Extensive simulations are conducted in highway scenarios and the results demonstrate the superiority of this offloading scheme. The proposed scheme achieves low delay performance and decreased computation cost over other competing schemes in typical urban and highway scenarios.

With a plethora of Internet of Things (IoT) applications for smart cities, encompassing and supporting several enabling technologies for real-time performance, an enormous amount of network packets faces the challenge of timely delivery. The IEEE 802.15.4 standard is one of the most popular and extensively adopted networking specifications for implementing different IoT applications and catering to several application-specific Quality of Service (QoS) requirements. Deterministic Synchronous Multi-channel Extension (DSME) is one of the Medium Access Control (MAC) protocols of IEEE 802.15.4 standard that facilitates stringent QoS through the allocation of DSME-Guaranteed Time Slots (GTSs) between a pair of devices. Interestingly, the standard does not define any mechanism for scheduling the DSME-GTSs, thereby opening several research opportunities. In this paper, we propose a Distributed Delay Aware Scheduling (DDAS) mechanism to increase the efficiency of the DSME MAC by using priority-based guaranteed time slots scheduling. DDAS assigns priority to the devices according to the criticality of time and number of associated devices, i.e., it identifies various flow deadlines and assigns GTS slots accordingly. The DDAS scheme aims to satisfy and adhere to various delay deadlines in the data flows of an IoT application. The proposed scheduling mechanism is shown to outperform other closely related schemes in terms of latency as well as energy consumption.

The IEEE 802.15.4 standard is one of the widely adopted networking specification for realizing different applications of Internet of Things (IoT). It defines several physical layer options and medium access control (MAC) sublayer protocols for low-power devices supporting low-data rates. One such MAC protocol is the deterministic and synchronous multichannel extension (DSME), which addresses the limitation on the maximum number of guaranteed time slots (GTSs) in 802.15.4-2011 MAC, and provides channel diversity to increase network robustness. However, beacon scheduling in peer-to-peer networks suffers from beacon slot collisions when two or more coordinators simultaneously compete for the same vacant beacon slot. In addition, the standard does not explore DSME-GTS scheduling (DGS) across multiple channels. This article addresses the beacon slot collision problem by proposing a nonconflicting beacon scheduling mechanism using association order (AO). Furthermore, a distributed multichannel DSME-GTS schedule is proposed that optimally assigns DSME-GTSs across different channels. The objective is to minimize the number of times-lots used while maximizing the usage of available channels. Through simulations, the proposed mechanisms’ performance is analyzed in terms of energy efficiency, transmission overhead, scheduling efficiency, throughput, and latency and is shown to outperform the other existing schemes.

The IEEE 802.15.4 standard is one of the widely adopted specifications for realizing different applications of the Internet of Things. It defines several physical layer options and Medium Access Control (MAC) sub-layer for devices with low-power operating at low data rates. As devices implementing this standard are primarily battery-powered, minimizing their power consumption is a significant concern. Duty-cycling is one such power conserving mechanism that allows a device to schedule its active and inactive radio periods effectively, thus preventing energy drain due to idle listening. The standard specifies two parameters, beacon order and superframe order, which define the active and inactive period of a device. However, it does not specify a duty-cycling scheme to adapt these parameters for varying network conditions. Existing works in this direction are either based on superframe occupation ratio or buffer/queue length of devices. In this article, the particular limitations of both the approaches mentioned above are presented. Later, a novel duty-cycling mechanism based on MAC parameters is proposed. Also, we analyze the role of synchronization schemes in achieving efficient duty-cycles in synchronized cluster-tree network topologies. A Markov model has also been developed for the MAC protocol to estimate the delay and energy consumption during frame transmission.

The new era of the Internet of Things (IoT) applications is driving the evolution of conventional Vehicle Ad-hoc Networks into the Internet of Vehicles (IoV). In the IoV networks, vehicles and the embedded sensory devices are the smart objects that are connected with other similar smart/IoT devices for data sharing and communication. The current specification of the IEEE 802.15.4 standard supports IoV/IoT application-specific Quality of Service (QoS) requirements. Specifically, the Deterministic and Synchronous Multi-channel Extension (DSME) MAC mode facilitates stringent latency and throughput performance through the use of DSME-Guaranteed Time Slots (GTS) allocation between two communicating devices. However, the standard does not explore DSME-GTS scheduling across the available channels, thereby making it an active research issue. This paper addresses the problem of DSME-GTS scheduling by proposing a distributed scheduling mechanism incurring minimal network overhead. The main challenge is to optimally use the available resources (channels and timeslots) and adhere to different flow deadlines. The proposed scheduling mechanism considers a 2-hop collision domain for channel assignment, and number of child devices for DSME-GTS allocations. The proposed scheduling mechanism's performance evaluation shows its efficiency in terms of energy consumption, transmission overhead, and channel utilization compared to other closely related schemes.

The IEEE 802.15.4 standard specifies two network topologies: 1) star and 2) cluster tree. A cluster-tree network comprises of multiple clusters that allow the network to scale by connecting devices over multiple wireless hops. The role of a cluster head (CH) is to aggregate data from all devices in the cluster and then transmit it to the overall personal area network (PAN) coordinator. This specific role of CH needs to be rotated among multiple coordinators in the cluster to prevent it from energy drain out. Prior works on CH rotation are either based on threshold energy levels or rely on periodic rotation. Both approaches have their respective limitations and, at times, result in unnecessary CH rotations or nonoptimal selection of CH. To address this, we propose a nonthreshold CH rotation scheme (NCHR), which incurs minimal rotation overhead. It supports topological changes, node heterogeneity, and can also handle CH failures. Through simulations and hardware implementation, the performance of the proposed NCHR scheme is analyzed in terms of network lifetime, CH rotation overhead, and the number of CH rotations. It is shown that the proposed scheme boosts network lifetime, incurs less rotation overhead, and needs fewer CH rotations compared to other related schemes.

In ad hoc networks, the communication is usually made through multiple hops by establishing an environment of cooperation and coordination among self-operated nodes. Such nodes typically operate with a set of finite and scarce energy, processing, bandwidth, and storage resources. Due to the cooperative environment in such networks, nodes may consume additional resources by giving relaying services to other nodes. This aspect in such networks coined the situation of noncooperative behavior by some or all the nodes. Moreover, nodes sometimes do not cooperate with others due to their social likeness or their mobility. Noncooperative or selfish nodes can last for a longer time by preserving their resources for their own operations. However, such nodes can degrade the network's overall performance in terms of lower data gathering and information exchange rates, unbalanced work distribution, and higher end-to-end delays. This work surveys the main roots for motivating nodes to adapt selfish behavior and the solutions for handling such nodes. Different schemes are introduced to handle selfish nodes in wireless ad hoc networks. Various types of routing techniques have been introduced to target different types of ad hoc networks having support for keeping misbehaving or selfish nodes. The major solutions for such scenarios can be trust-, punishment-, and stimulation-based mechanisms. Some key protocols are simulated and analyzed for getting their performance metrics to compare their effectiveness.

The IEEE 802.15.4 standard is one of the most widely used link layer technology for building Internet of Things (IoT). It specifies several physical layer options and MAC layer for meeting low-power and low-rate requirements of devices deployed in a network of IoT. The standard also specifies a synchronization scheme for devices connected in a star topology, operating in beacon-enabled (BE) mode using periodic beacons. The BE mode facilitates synchronization among devices for data transmission and is suitable for large networks to establish low duty-cycles. Absence of a such a scheme for a cluster-tree network has confined its application only to nonbeacon mode. The challenge here is to schedule beacon frame transmissions of multiple devices in a nonoverlapping manner to avoid beacon collisions. This paper tackles the problem of synchronization by proposing localized beacon synchronization (LBS) scheme, a distributed technique for beacon scheduling in cluster-tree network topologies. LBS uses 2-hop information and association order to compute beacon transmission offsets that better utilize the available time slots, incur fewer transmissions, and is highly scalable. Further, we analytically show that the schedulability of the proposed scheme is higher compared to other related schemes. In addition, we also address the important issue of resynchronization that has been ignored in all of the prior works. The proposed resynchronization mechanisms consider the interdependencies between synchronization and duty-cycling schemes and are shown to significantly lower the synchronization overhead when synchronization among devices is lost.

The role of cluster-head in an IEEE 802.15.4 cluster-tree network is to aggregate data from various devices in the cluster and cumulatively transmit to the PANC. This is an energy efficient way of sending data compared to individual reporting of devices independently. Cluster-head coordinators expend more energy compared to other coordinators in the cluster as they have to remain active for longer duration and carry out tasks like aggregation and transmission. Therefore this role of a cluster-head has to be periodically rotated among different coordinators to prevent exhaustion of a particular coordinator's energy and to extend the overall network lifetime. Few of the works done in this direction consider the existence of single hop transmission link to the PANC. Majority of other works designed for wireless sensor networks (WSNs) base the cluster-head rotation decision on threshold of available residual-energy in a coordinator. In this paper, we present a non- threshold based cluster-head rotation scheme that makes a rotation decision based on network- lifetime. It considers the residual energy, transmission cost and aggregation cost from associated coordinators and end-devices in synchronized IEEE 802.15.4 cluster-tree networks. Through simulations, we show that the proposed mechanism extends the overall network lifetime, outperforming other approaches.

The IEEE 802.15.4 standard is a widely adopted standard for low power wireless personal area networks. It defines several medium access control layer functionalities including channel access, beacon management, guaranteed time slot management, etc. These issues are relatively straight forward in star topology, but the similar tasks pose several challenges in a peer-to-peer cluster tree network. Specifically, beacon synchronization and duty cycling schemes that are influenced by superframe parameters need to operate effectively as they serve as major energy saving avenues. The former that is part of beacon management process allows a device to synchronize its transmissions with a coordinator to facilitate better channel utilization. Further, duty-cycling allows devices to enter low-power mode by scheduling their sleep period. Lack of these schemes in the standard for cluster-tree networks has motivated research in this direction. However, all the related works have aimed to address the problem of duty-cycling and synchronization independently without considering the interdependencies between them. These dependencies arise due to the common superframe parameters. In this paper, we first analyze various works carried out to address beacon synchronization and duty-cycling issue in IEEE 802.15.4 networks. Later, we establish a co-relation between these two mechanisms and show how the former effects the later and vice-versa. The analytical and simulation results allow us to understand the existing schemes better and further assist in the design of aforementioned schemes to maximize energy savings.

The IEEE 802.15.4 standard does not allow to make dynamic adjustments to the inactive portion of the superframe, thus affecting the duty cycles of the coordinator and all the devices attached to it. Prior works in this direction are either based on superframe occupation ratio or buffer occupancy/queue length of the transmitting nodes. In this paper, we present the respective limitations of both these schemes that lead to sub-optimal MAC parameter (BO and SO) settings and later propose a dynamic duty cycling mechanism based on MAC parameters (macMinBE, macMaxCSMABackoffs and macMaxFrameRetries). A Markov model is developed for IEEE 802.15.4 CSMA-CA that is used to analytically estimate the delay and energy consumption during transmission of frames using the MAC parameters.

Duty-cycling schemes allow devices to dynamically adjust their active period to conserve energy. On the other hand, synchronization schemes allow multiple coordinators to schedule their transmissions in order to prevent the overlapping of superframe schedules. In this paper, we analyze the impact of a synchronization mechanism on duty-cycling schemes in an IEEE 802.15.4 cluster tree network. We show the necessity of an operational synchronization mechanism when devices adopt an independent duty-cycling approach so that the later accounts to effective energy savings.

The IEEE 802.15.4 standard does not support adaptive duty cycles. Prior works in this direction are either based on superframe occupation ratio or buffer occupancy/queue length of the transmitting nodes. In this paper, we find the respective limitations of both these schemes that lead to sub-optimal duty cycle parameter settings. Afterward, a duty cycling algorithm is proposed wherein the channel state is estimated with the help of MAC parameters (macMinBE, macMaxCSMABackoffs, and macMaxFrameRetries) that induces dynamic adaptation of duty-cycle among the nodes. A Markov model is developed for IEEE 802.15.4 carrier sense multiple access with collision avoidance (CSMA-CA) to estimate the delay and energy consumption while transmitting frames using MAC parameters.